Methods for diagnosis, prognosis and treatment of primary and metastatic basal-like breast cancer and other cancer types

ABSTRACT

In one embodiment, methods of theranostic classification of a breast cancer tumor are provided, wherein the classification is determined by detecting an expression level of FOXC1. In other embodiments, methods for predicting a prognosis of a basal-like breast cancer and methods of treating a basal-like breast cancer are provided. In other embodiments, methods for diagnosing metastatic breast cancer using the expression ratio of FOXC1/FOXA1 in a population of breast cancer tumor cells are provided. The methods also entail administering a treatment for metastatic breast cancer if the expression ratio of FOXC1/FOXA1 in the population of breast cancer tumor cells is elevated as compared to a control. Other embodiments provide methods for treating breast cancer with a proteasome inhibitor alone or in combination with a Wnt inhibitor in subjects with tumor cells expressing FOXC1 in a subject.

PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No.15/617,333, filed Jun. 8, 2017, now pending, which is acontinuation-in-part of U.S. patent application Ser. No. 14/749,419,filed Jun. 24, 2015, now abandoned, which is a continuation of U.S.patent application Ser. No. 14/040,034, filed Sep. 27, 2013, nowpatented (U.S. Pat. No. 9,074,253); which is a continuation of U.S.patent application Ser. No. 12/852,453, filed Aug. 7, 2010, nowabandoned; which is a continuation of International Application No.PCT/US10/44817, filed Aug. 6, 2010 and now expired; which claimspriority to U.S. Provisional Patent Application No. 61/231,984, filed onAug. 6, 2009 and now expired, all of which are incorporated by referenceas if fully set forth herein.

Application Ser. No. 15/617,333 is also a continuation-in-part ofInternational Application No. PCT/US15/64574, filed Dec. 8, 2015; whichclaims priority to U.S. Provisional Patent Application Nos. 62/089,228,filed Dec. 8, 2014, 62/089,816, filed Dec. 9, 2014, all of which areincorporated herein by reference in their entirety, as if fully setforth herein.

Application Ser. No. 15/617,333 is also a continuation-in-part ofInternational Application No. PCT/US15/64573, filed Dec. 8, 2015; whichclaims priority to United States Provisional Application Nos.62/089,214, filed Dec. 8, 2014, and 62/090,323, filed Dec. 10, 2014, allof which are incorporated herein by reference in their entirety, as iffully set forth herein.

SEQUENCE LISTING

This application contains a Sequence Listing, which was submitted inASCII format via EFS-Web, and is hereby incorporated by reference in itsentirety. The ASCII copy, created on Jul. 1, 2020, is named 2020-07-01Replacement Sequence Listing 079877-8010US01 and is 25 KB in size.

BACKGROUND

Diversity of molecular alterations, cellular compositions and clinicaloutcomes in cancer creates a major challenge in cancer treatment withrespect to providing accurate diagnostic, prognostic, and predictiveinformation. Tumors are typically described histopathologically usingthe tumor-node-metastasis (TNM) system. This system, which uses the sizeof the tumor, the presence or absence of tumor in regional lymph nodes,and the presence or absence of distant metastases, assigns a stage tothe tumor as described by the American Joint Committee on Cancer (AJCC).The assigned stage is used as the basis for prognostication and forselection of appropriate therapy. However, this approach has manylimitations. Tumors with similar TNM stage and histopathologicappearance can exhibit significant variability in terms of clinicalcourse and response to therapy. For example, some tumors are veryaggressive while others are not. Some tumors respond readily to hormonaltherapy or chemotherapy while others are resistant.

The use of tumor biomarkers has provided an additional approach fordividing certain tumor types into subclasses. For example, one factorconsidered in prognosis and in treatment decisions for breast cancer isthe presence or absence of the estrogen receptor (ER) in tumor samples.ER-positive breast cancers typically respond much more readily tohormonal therapies such as tamoxifen than ER-negative tumors. Thoughuseful, this biomarker provides information for only a specific subsetof breast cancers, leaving other subsets unaddressed.

Gene expression profiling has been successful in delineating specificbreast cancer intrinsic molecular subtypes (Perou et al. 2000). Thisrepresents a significant advance in the understanding of breast cancer,the most commonly diagnosed cancer in women worldwide (Landis et al.,1999) and a disease that has proven to be quite heterogeneous in termsof its clinical presentation and features. Groups of breast cancerpatients with distinct differences in their prognostic profiles have nowbeen found to have equally distinct biologic and/or molecular profilesto help explain their associated clinical outcomes. This offers atremendous opportunity to develop personalized therapeutics targetingthe specific tumor biology associated with a specific molecular subtypeof breast cancer. One particular molecular subtype that has garneredconsiderable interest is basal-like breast cancer (BLBC).

Although first reported more than 20 years ago on the basis ofimmunohistochemical (IHC) detection of basal cytokeratins (CK), thissubtype again became notable after transcriptomic analysis of breastcancer confirmed its existence as a distinct molecular entity withinbreast cancer. While it differs substantially from the other delineatedmolecular subtypes in terms of its molecular makeup, the reason it hascaptured the attention of cancer biologists and clinicians alike is onaccount of its uniformly poor prognosis and lack of targeted therapyoptions. BLBC displays significant overlap with “triple-negative” breastcancer—a pathologic entity defined based on the absence of well-knownbreast cancer biomarkers estrogen receptor (ER), progesterone receptor(PR) and human epidermal growth factor receptor-2 (HER2). It isestimated that 60% to 90% of triple-negative breast cancers are BLBC.However BLBC is not synonymous with triple-negative breast cancer.Patients with BLBC are often younger, are more likely to be ofAfrican-American descent (Carey et al. 2006; Ihemelandu et al. 2007;Ihemelandu et al. 2008), are more likely to be BRCA1 mutation positive(Rakha et al. 2009), frequently develop distant metastatic disease tothe brain and/or lung within 3-5 years of initial presentation (Wang etal. 2005) and have poor overall survival (Carey et al. 2006). In fact,the development of distant metastatic disease and subsequent deathappears to be independent of initial presenting nodal status, as themajority of patients are lymph node negative at the time of initialdiagnosis (Dent et al. 2007).

Currently the most effective biomarkers in routine clinical practice aretheranostic biomarkers. Theranostic biomarkers provide information withrespect to diagnosis (determination of the cancer biologic subtype),prognosis (determination of the clinical outcome) and therapeuticprediction (determination of therapeutic efficacy). Theranosticbiomarkers are functionally most central and pivotal in the network ofbiomolecules that control the biology of their specific biologicsubtype. Hence, targeted therapy directed towards a theranosticbiomarker has a profound effect on clinical outcomes.

In breast cancer an example of a theranostic biomarker is ER. Itaccurately diagnoses “lumina!” breast cancer patients (ER-positive),accurately prognosticates their outcome, and predicts their favorableresponse to tamoxifen, a drug that specifically targets ER. Prior to theintroduction of tamoxifen therapy, ER-positive breast cancer patientshad a poor prognosis. Their prognosis dramatically improved aftertherapy with tamoxifen became standard of care for such patients.Therefore, the most important component of a theranostic biomarker isthe diagnosis it offers. Because with diagnosis comes prediction oftherapeutic efficacy, which ultimately determines patient prognosis.While prognosis may change depending on advancements in therapy, thediagnosis of a biologic subtype, and therefore its target(s) for therapywill remain immutable. Moreover, the prognosis offered by a theranosticbiomarker is more accurate than that offered by a non-theranosticbiomarker. This is because theranostic biomarkers predict clinicaloutcomes that are very specific to the biology of the cancer subtype.For example, ER-positive status very specifically reflects the currentfavorable prognosis associated only with the luminal subtype because ittakes into account subtype-specific treatment with anti-ER therapy (e.g.tamoxifen). Therefore, theranostic biomarkers offer superior prognosis.

Whole genome profiling technologies such as gene expression profiling(transcriptomics) have greatly expanded our knowledge of the genes andgenetic pathways associated with the development and progression ofcancer. Based on this knowledge, several commercialized multigeneprognostic tests have entered the complex and expanding landscape of thecancer in vitro diagnostics (IVD) market. These tests contain manygenes, only some of which indeed have critical functional importance tothe survival and maintenance of the malignant phenotype. Such tests areunable to offer a refined understanding of the underlying biology of aspecific subtype. In other words, the main drawback of such multigeneprognostic tests is that they are not theranostic. They do not provide adiagnosis of a specific biologic subtype, and therefore they do notoffer insight with regard to subtype-specific treatment. As a result,the prognostic value they offer is only an approximation across multiplesubtypes. This is in contrast to a theranostic biomarker whoseprognostic value is derived from a single subtype, and is therefore moreprecise and accurate.

Therefore the discovery and elucidation of theranostic biomarkers forBLBC and other cancers is important for the improvement of theclassification of tumors and the treatment of cancer patients.

Further, distant metastatic spread of cancer cells to other organs fromthe primary site of origin currently constitutes the most significantcontributor to cancer-related morbidity and mortality.Epithelial-to-mesenchymal transition (EMT) is a biologic transformationof cancer cells from a non-migratory phenotype to a migratory one, andis thought to initiate the metastatic cascade in cancer. EMT has alsobeen reported to trigger acquisition of stem cell traits in breastcancer. There is a need to develop new approaches to effectivelydiagnose and treat epithelial-to-mesenchymal transition of cancer cellsand breast cancer metastasis.

Moreover, cancer stem cells (CSCs) are considered to be an importantcontributing factor towards treatment failure, cancer recurrence, andmortality. CSCs are known to be more enriched in the basal-like andclaudin-low subtypes of breast cancer. There is a need to develop newapproaches to effectively target and treat basal-like and claudin-lowsubtypes of breast cancer.

SUMMARY

In one embodiment, a method of theranostic classification of a breastcancer tumor is provided, the method comprising obtaining a breastcancer tumor sample from a subject, detecting an expression level ofFOXC1, comparing the expression level of FOXC1 to a predetermined cutofflevel, and classifying the breast cancer tumor sample as belonging to atheranostic basal-like breast cancer tumor subtype or a theranostichybrid basal-like breast cancer tumor subtype when the expression levelof FOXC1 is higher than the predetermined cutoff level.

In one embodiment, the method of theranostic classification of a breastcancer tumor may further comprise determining an expression status forestrogen receptor (ER), progesterone receptor (PR) and human epidermalgrowth factor receptor 2 (HER2) and classifying the breast cancer tumorsample as belonging to a theranostic hybrid basal-like/HER2+ breastcancer tumor subtype when the expression status of ER is negative (ER−),the expression status of PR is negative (PR−), the expression status ofHER2 is positive (HER2+) and the expression level of FOXC1 is higherthan the predetermined cutoff level.

In another embodiment, the method of theranostic classification of abreast cancer tumor may further comprise determining an expressionstatus of ER, PR, and HER2 of the breast cancer tumor sample andclassifying the breast cancer tumor sample as belonging to a theranostichybrid basal-like/triple-negative breast cancer tumor subtype when theexpression status of ER is negative (ER−), the expression status of PRis negative (PR−), the expression status of HER2 is negative (HER2−) andthe expression level of FOXC1 is higher than the predetermined cutofflevel.

In another embodiment, the method of theranostic classification of abreast cancer tumor may further comprise determining an expressionstatus of ER, PR, and HER2 of the breast cancer tumor sample andclassifying the breast cancer tumor sample as belonging to a theranostichybrid basal-like/luminal breast cancer tumor subtype when theexpression status of ER is positive (ER+), the expression status of PRis negative or positive (PR−/PR+), the expression status of HER2 isnegative or positive (HER2−/HER2+) and the expression level of FOXC1 ishigher than the predetermined cutoff level.

In one embodiment, a method for predicting a prognosis of a basal-likebreast cancer is provided, the method comprising obtaining a breastcancer tumor sample from a subject, detecting an expression level ofFOXC1, comparing the expression level of FOXC1 to a predetermined cutofflevel, and predicting a poor prognosis of the basal-like breast cancerwhen the expression level of FOXC1 is higher than the predeterminedcutoff level.

In some embodiments, the basal-like breast cancer is a hybridbasal-like/HER2+ breast cancer and the predetermined cutoff level isdetermined by a 50th percentile level of FOXC1 expression levels for adataset of breast cancer tumors, the dataset comprising tumors having aHER2+ status.

In other embodiments, the basal-like breast cancer is a hybridbasal-like/luminal breast cancer and the predetermined cutoff level isdetermined by a 50th percentile level of FOXC1 expression levels for adataset of breast cancer tumors, the dataset comprising tumors having anER+ status.

In other embodiments, the basal-like breast cancer is a hybridbasal-like/triple-negative breast cancer and the predetermined cutofflevel is determined by a 50th percentile level of FOXC1 expressionlevels for a dataset of breast cancer tumors, the dataset comprisingtumors having an ER−/PR−/HER2− status.

In some embodiments, the prognosis is overall survival, recurrence freesurvival, a propensity of developing a distant metastasis, a time to adistant metastasis such as brain metastasis, a propensity for resistanceto a targeted cancer therapy (e.g., trastuzumab (Herceptin®, tamoxifenor an aromatase inhibitor), wherein a propensity for resistance to atargeted cancer therapy may be a predictor of resistance or decreasedefficacy.

In one embodiment, a method of treating a basal-like breast cancer isprovided, the method comprising administering to a subject having abasal-like breast cancer a pharmaceutical composition, the compositioncomprising a pharmaceutically acceptable carrier and a therapeuticallyeffective amount of a substance that inhibits FOXC1 expression and/oractivity. In one embodiment, the basal-like breast cancer being treatedis a hybrid basal-like/triple-negative breast cancer tumor subtype suchas a hybrid basal-like/HER2+ breast cancer tumor subtype or hybridbasal-like/luminal breast cancer tumor subtype.

In one embodiment, the pharmaceutically acceptable carrier is aPEGylated immunoliposome that encapsulates the substance. In anotherembodiment, the substance is selected from the group consisting of ananti-FOXC1 antibody or functional fragment thereof, a small molecule oran anti-FOXC1 shRNA, siRNA or RNAi.

In some embodiments, the pharmaceutical composition further comprises atherapeutically effective amount of a substance that inhibits HER2expression and/or activity such as trastuzumab (Herceptin®). In otherembodiments, the pharmaceutical composition further comprises atherapeutically effective amount of a substance that inhibits ERexpression and/or activity such as tamoxifen or an aromatase inhibitor.

Provided herein in certain embodiments are methods of treatingmetastatic breast cancer in a subject. In certain embodiments, themethods may include detecting an expression level of FOXC1 in apopulation of breast cancer tumor cells from the subject, detecting anexpression level of FOXA1 in the population of breast cancer tumorcells, and administering a treatment for metastatic breast cancer to thesubject if the expression ratio of FOXC1/FOXA1 in the population ofbreast cancer tumor cells is elevated as compared to a control. Incertain embodiments, determining the expression level of FOXC1 and FOXA1in the breast cancer tumor cells may be performed via quantitativeRT-PCR (qRT-PCR) or RNA sequencing (RNA-Seq). In certain embodiments,the control may be a cutoff expression ratio which is established usinga set of FOXC1/FOXA1 expression ratios from a population of patientshaving a cross-section of all types of breast cancer and the cutoffexpression ratio may fall at the 75th percentile line, the 80thpercentile line, the 85th percentile line, the 90th percentile line, the95th percentile line, or at higher than the 95th percentile line. Incertain embodiments, the control may be a cutoff expression ratio whichis established using a set of FOXC1/FOXA1 expression ratios from apopulation of patients having node-negative breast cancer and the cutoffexpression ratio may fall at the 80th percentile line. In certainembodiments, the expression ratio of FOXC1/FOXA1 in the population ofbreast cancer tumor cells may be detected at a pre-symptomatic stage ofearly breast cancer metastasis. In certain embodiments, the treatmentmay be a therapeutically effective amount of one or more therapeuticagents. In certain embodiments, the one or more therapeutic agents maybe selected from chemotherapeutic agents, therapeutic antibodies andfragments thereof, toxins, radioisotopes, enzymes, nucleases, hormones,immunomodulators, antisense oligonucleotides, nucleic acid molecules,chelators, boron compounds, photoactive agents and dyes.

Also provided herein in certain embodiments are methods of diagnosingmetastatic breast cancer in a subject. In certain embodiments, themethods may include detecting an expression level of FOXC1 in apopulation of breast cancer tumor cells from the subject, detecting anexpression level of FOXA1 in the population of breast cancer tumorcells, and diagnosing the subject as having metastatic breast cancer ifthe expression ratio of FOXC1/FOXA1 in the population of breast cancertumor cells is elevated as compared to a control. In certainembodiments, determining the expression level of FOXC1 and FOXA1 in thebreast cancer tumor cells may be performed via quantitative RT-PCR(qRT-PCR) or RNA sequencing (RNA-Seq). In certain embodiments, thecontrol may be a cutoff expression ratio which is established using aset of FOXC1/FOXA1 expression ratios from a population of patientshaving a cross-section of all types of breast cancer and the cutoffexpression ratio may fall at the 75th percentile line, the 80thpercentile line, the 85th percentile line, the 90th percentile line, the95th percentile line, or at higher than the 95th percentile line. Incertain embodiments, the control may be a cutoff expression ratio whichis established using a set of FOXC1/FOXA1 expression ratios from apopulation of patients having node-negative breast cancer and the cutoffexpression ratio may fall at the 80th percentile line. In certainembodiments, the expression ratio of FOXC1/FOXA1 in the population ofbreast cancer tumor cells may be detected at a pre-symptomatic stage ofearly breast cancer metastasis. In certain embodiments, the methods mayfurther include administering a treatment for metastatic breast cancerif the expression ratio of FOXC1/FOXA1 in the population of breastcancer tumor cells is elevated as compared to the control. In certainembodiments, the treatment may be a therapeutically effective amount ofone or more therapeutic agents. In certain embodiments, the one or moretherapeutic agents may be selected from chemotherapeutic agents,therapeutic antibodies and fragments thereof, toxins, radioisotopes,enzymes, nucleases, hormones, immunomodulators, antisenseoligonucleotides, nucleic acid molecules, chelators, boron compounds,photoactive agents and dyes.

Also provided herein in certain embodiments are methods for treatingcancer in a subject. In certain embodiments, the methods may comprisedetermining an expression level of FOXC1 in a population of tumor cellsobtained from the subject and administering one or more proteasomeinhibitors, and optionally, one or more Wnt inhibitors, to the subjectto treat the cancer if the population of tumor cells expresses FOXC1. Incertain embodiments, the proteasome inhibitor may be bortezomib. Incertain embodiments, the one or more Wnt inhibitors may be iCRT3. Incertain embodiments, the cancer may be a basal-like breast cancer. Incertain embodiments, the basal-like breast cancer may be triple negativebreast cancer. In certain embodiments, the basal-like breast cancer maybe ER positive breast cancer. In certain embodiments, the basal-likebreast cancer may be HER2 positive breast cancer. In certainembodiments, the cancer may be claudin-low cancer. In certainembodiments, determining the expression level of FOXC1 in the populationof tumor cells may be performed via quantitative RT-PCR (qRT-PCR).

Also provided herein in certain embodiments are combination therapiescomprising one or more Wnt inhibitors and one or more proteasomeinhibitors. In certain embodiments, the one or more proteasomeinhibitors may be bortezomib. In certain embodiments, the one or moreWnt inhibitors may be iCRT3. In certain embodiments, the one or more Wntinhibitors may be a therapeutically effective amount. In certainembodiments, the one or more proteasome inhibitors may be atherapeutically effective amount. [0017] Also provided herein aremethods for treating cancer in a subject comprising administering one ormore proteasome inhibitors, and optionally, one or more Wnt inhibitors,to the subject to treat the cancer. In certain embodiments, the one ormore proteasome inhibitors may be bortezomib. In certain embodiments,the one or more Wnt inhibitors may be iCRT3. In certain embodiments, thecancer may be a basal-like breast cancer. In certain embodiments, thebasal-like breast cancer may be triple negative breast cancer. Incertain embodiments, the basal-like breast cancer may be ER positivebreast cancer. In certain embodiments, the basal-like breast cancer maybe HER2 positive breast cancer. In certain embodiments, the cancer maybe claudin-low cancer.

BRIEF DESCRIPTION OF THE DRAWINGS

This application contains at least one drawing executed in color. Copiesof this application with color drawing(s) will be provided by the Officeupon request and payment of the necessary fees.

FIGS. 1A-1D show differential expression of FOXC1 in human breast cancersubtypes. FIG. 1A, values of normalized signal intensity(baseline-to-zero-transformed) for basal-like subtype-associated genesfrom the Richardson et al. data set (Richardson et al. 2006). Numbersrepresent different subgroups: (1), normal; (2), luminal A/B; (3), HER2;(4), basal-like. FIG. 1B, boxplot of FOXC1 values (normalized signalintensity) in normal breast tissue and luminal, HER2, and basal-liketumors of the same data set. Statistical significance was determinedusing ANOVA. FIG. 1C, boxplot of FOXC1 values from the Hess et al. dataset with known ER, PR, and HER2 status (Hess et al. 2006). See FIG. 3legends for description of boxplots. Statistical significance wasdetermined using ANOVA. FIG. 1D, gene expression heat maps of theIvshina et al. data set (Ivshina et al. 2006) hierarchically clusteredby IGS display the expression profile of the FOXC1 signature.

FIG. 2 shows differential expression of FOXC1 in human breast cancersubtypes. Values of normalized signal intensity for 12 reportedbasal-like markers from a representative dataset (Richardson et al.2006) are presented. Numbers represent different subgroups: (1), normal;(2), luminal A/B; (3), HER2; (4), Basal-like. The corresponding heat mapis shown below.

FIGS. 3A-3D show differential expression of FOXC1 according to molecularsubtypes or triple negative status. FIG. 3A. Boxplot of FOXC1 values(normalized signal intensity) in luminal A/B, HER2, and basal-liketumors of the Ivshina et al. dataset (Ivshina et al. 2006). The line inthe center of each box represents the median value of the distribution,and the upper and lower ends of the box are the upper (75th) and lower(25th) quartiles, respectively. The whiskers extend to the most extremedata point that is less than 1.5 times the interquartile range from thebox. Statistical significance was determined using ANOVA. Table of FOXC1high (>90th percentile) and FOXC1 low (<90_(th) percentile) statusversus molecular subtypes. Chi square P<0.0001. FIG. 3B. Boxplot ofFOXC1 values (normalized signal intensity) in luminal A/B, HER2, andbasal-like tumors of the Miller et al. dataset (Miller et al. 2005).Statistical significance was determined using ANOVA. Table of FOXC1 high(>90_(th) percentile) and FOXC1 low (<90_(th) percentile) status versusmolecular subtypes. Chi square P<0.0001. FIG. 3C. Boxplot of FOXC1values (normalized signal intensity) in luminal A/B, HER2, andbasal-like tumors of the van de Vijver et al. dataset (van de Vijver etal. 2002). Statistical significance was determined using ANOVA. Table ofFOXC1 high (>90_(th) percentile) and FOXC1 low (<90_(th) percentile)status versus molecular subtypes. Chi square P<0.0001. FIG. 3D. Boxplotof FOXC1 values (normalized signal intensity) in triple-negative andnon-triple-negative tumors of the Hess et al. dataset (Hess et al.2006). Statistical significance was determined using ANOVA. Table ofFOXC1 high (>90_(th) percentile) and FOXC1 low (<90_(th) percentile)status versus triple-negative status. Chi square P<0.0001.

FIG. 4 shows an association of the FOXC1 gene signature with basal-likebreast cancer. Gene expression heat maps of a 251-sample human breastcancer cDNA microarray dataset (Miller et al. 2005) hierarchicallyclustered by IGS demonstrate the overall expression profile of theFOXC1-associated 30 genes.

FIG. 5 shows unsupervised clustering by the FOXC1 gene signatureidentifies the basal-like subgroup. A 249-sample human breast cancercDNA microarray dataset (Ivshina et al. 2006) was clustered by IGS andthe FOXC1 gene signature respectively. The basal-like subtype clustersare indicated with red bars.

FIGS. 6A-6C illustrate FOXC1 protein expression in BLBC. FIG. 6A,representative immunohistochemical images of a basal-like sample frombreast cancer tissue microarrays stained for ER, HER2, CK5/6, CK14, andFOXC1. FOXC1 protein was not detected in non-triple-negative specimens.FIG. 6B, Venn diagram showing the association between FOXC1 andcytokeratin (CK5/6 and/or CK14) immunohistochemistry status intriple-negative tumors. FIG. 6C, immunoblotting of FOXC1 in normal HMECsand luminal (MCF-7, T47D, and ZR75), HER2-overexpressing (SKBR3 andHCC202), or BLBC cell lines.

FIG. 7 illustrates that FOXC1 is overexpressed in basal-like breastcancer cell lines. Gene expression heat map from cDNA microarrayanalysis of 51 human breast cancer cell lines. Displayed is the samepanel of 12 genes as in FIG. 2. MSN—Moesin, KRT5—Cytokeratin 5/CK5/6,CDH3—P-cadherin, CRYAB—αB-crystallin, KRT14—cytokeratin 14/CK14,KRT17—cytokeratin 17/CK17.

FIGS. 8A-8D illustrate prognostic significance of FOXC1 in human breastcancer. FIG. 8A, Kaplan-Meier curves of overall survival using data fromthe van de Vijver et al. data set (van de Vijver et al. 2002). Overallsurvival was stratified by molecular subtypes (left), the FOXC1 genesignature (middle), and FOXC1 mRNA levels (right). FIG. 8B, Kaplan-Meiercurves of overall survival in lymph node-negative patients from the samedata set. FIG. 8C, Kaplan-Meier curves of brain (left) and bone (right)metastasis-free survival using data from the Wang et al. data set (Wanget al. 2005) stratified by molecular subtypes. FIG. 8D, Kaplan-Meiercurves of brain and bone metastasis-free survival stratified by FOXC1mRNA levels from the same data set.

FIGS. 9A-9C illustrate prognostic power of FOXC1 expression in humanbreast cancers. FIG. 9A, Kaplan-Meier curves of overall survival usingdata from a 232-sample microarray dataset (Herschkowitz et al. 2007)with linked clinical information. FIG. 9B, Kaplan-Meier curves ofoverall survival using data from a 122-sample microarray dataset (Sorlieet al. 2003) with linked clinical information. FIG. 9C, Kaplan-Meiercurves of overall survival using data from a 159-sample microarraydataset (Pawitan et al. 2005) with linked clinical information. Overallsurvival is displayed according to molecular subtypes (left) and FOXC1mRNA levels (right).

FIG. 10 is a receiver operator curve (ROC)-area under curve (AUC) forFOXC1 expression in predicting basal-like breast cancer. (Parker et al.2009)

FIGS. 11A-11D show the effects of FOXC1 overexpression and knockdown inbreast cancer cells. FIG. 11A, cell proliferation (left), migration(middle), and invasion (right) of FOXC1- or vector-overexpressingMDA-MB-231 cells. Columns, mean (n=3); bars, SD. *, P<0.05, versus thecontrol. FIG. 11B, cell proliferation, migration, and invasion ofcontrol or FOXC1 shRNA-expressing 411 cells. *, P<0.05, versus thecontrol. FIG. 11C, morphologies of control and FOXC1 shRNA 411 cells inmonolayer culture. FIG. 11D, representative images of control and FOXC1shRNA 411 cells grown in three-dimensional (3-D) Matrigel (left) andsoft agar (right). Bar, 135 μm.

FIGS. 12A-12E show the effects of FOXC1 overexpression in human breastcancer cells and MCF-10A cells. FIG. 12A, FOXC1 was stably transfectedinto MCF-7 breast cancer cells. Cell proliferation (left),anchorage-independent growth (middle-left), migration (middle-right),and invasion (right) of FOXC1- or vector-expressing cells were measuredusing MTT, soft agar, and Boyden chamber assays. *, P<0.05 versus thevector control. FIG. 12B, expression of cyclin D1 and fibroblasticmarkers in MDA-MB-231 cells overexpressing FOXC1 or the control vectorwas examined by immunoblotting. FIG. 12C, levels of β4 and β1 integrinsin MDA-MB-231 cells overexpressing FOXC1 or the control vector weremeasured by flow cytometry. *, P<0.05 versus the vector control. Ofnote, same results were obtained with MCF-7 cells. FIG. 12D, expressionof MMP2 and MMP9 was measured by ELISA. Each bar represents mean±SD(n=3). *, P<0.05 versus the vector control. FIG. 12E, morphologies ofMCF-10A human mammary epithelial cells overexpressing the vector orFOXC1 (left) and immunoblotting of luminal (E-cadherin) and basal(P-cadherin) markers in the same cells.

FIGS. 13A-13D show the effects of FOXC1 knockdown in human breast cancercells. FIG. 13A, FOXC1 protein levels were compared in MCF-7, BT549, and411 breast cancer cells (refer to FIG. 2C). FIG. 13B, immunoblotting ofFOXC1 in 411 cells expressing control or FOXC1 shRNA. FIG. 13C, cellproliferation (left), migration (middle), and invasion (right) ofcontrol or FOXC1 shRNA19 expressing BT549 cells were measured using MTTand Boyden chamber assays. *, P<0.05 versus the control. FIG. 13D,immunoblotting of FOXC1 in BT549 cells expressing control or FOXC1shRNA.

FIG. 14 is a Kaplan-Meier curve of overall survival using microarraydata from 58 HER2-amplified tumors. Semiquantitative FOXC1 mRNAexpression above the 50^(th) percentile was found to be a significantpredictor of poor survival (p=0.0313 on univariate analysis). Onmultivariate analysis, when controlled for age, tumor size and nodalstatus, FOXC1 mRNA expression greater than 50^(th) percentile cutoffvalue was an independent prognosticator of poor survival (HR 2.54, 95%Cl 1.21-5.33, p=0.0138). Nodal status and age were not significantprognosticators on multivariate analysis (Staaf et al. 2010).

FIG. 15 is a flow diagram of patient identification, sample collectionand tissue processing for immunohistochemical assessment of ER, PR,HER2, CK5/6, CK14 and FOXC1.

FIGS. 16A-16B show Kaplan Meier curves of 5-year overall survival ofbreast cancer patients grouped according to FIG. 16A FOXC1 proteinexpression status as assessed on standard immunohistochemistry, whereinpositive expression of FOXC1 was shown to be a significant predictor ofoverall survival, independent of the cutoff value employed; and FIG. 16Bsurrogate immunohistochemical biomarker models of molecular subtypeutilizing 3 different cutoff values to define positive expression ofFOXC1. Level of protein expression as assessed by IHC was given a scoreof 0 (negligible or no expression) to 3 (high expression). The threecutoff values were: 0 vs. 1, 2, 3; 0, 1 vs. 2, 3; or 1, 2, 3 vs. 3.

FIGS. 17A-17B show Kaplan Meier curves of 5-year overall survival ofbreast cancer patients grouped according to FIG. 17A Triple negativephenotype (TNP) status, Basal cytokeratin (CK) expression status andFOXC1 protein expression status as assessed on standardimmunohistochemistry; and FIG. 17B 3 surrogate immunohistochemicalbiomarker panel models of breast cancer molecular subtype—1) the classic3-biomarker panel comprising of ER, PR and HER2, 2) a 5-biomarker panelcomprising of the above receptors in combination with traditionalbasal-like biomarkers, basal CK5/6 and CK14, and 3) a 4-biomarker panelcomprising of ER, PR and HER2, in combination with FOXC1.

FIGS. 18A-18B illustrate that FOXC1 expression is negatively associatedwith ERα expression in human breast cancer. FIG. 18A Microarray dataanalyses of the association between FOXC1 and ERα expression in humanbreast cancers. FOXC1 mRNA levels in breast cancer are shown in boxplots. The student's t test was conducted using the Oncomine software.Results from six representative data sets [(Ginestier et al., 2006; Luet al., 2008; Richardson et al., 2006; Sorlie et al., 2001; Zhao et al.,2004) and the Oncology-Breast Samples Project database (Bittnet et al.)of the International Genomics Consortium (IGC) athttps://expo.intaen.ora/expo/public] are presented. FIG. 18B Expressionof FOXC1 in ERα-positive or -negative human breast cancer cell lines isshown by immunoblotting.

FIGS. 19A-19D show FOXC1 mRNA levels in human breast cancer tissuesshown in box plots. Microarray data analyses of the association betweenFOXC1 and ERα expression in human breast cancers are shown for FIG. 19Athe Pollock et al. data set; FIG. 19B the Perou et al. data set; FIG.19C the Sorlie et al. data set; and FIG. 19D the Schuet et al. data set.FOXC1 expression is shown to be negatively associated with ERαexpression in breast cancer. The student's t test was conducted usingthe Oncomine software.

FIGS. 20A-20D illustrate that FOXC1 downregulates ERα expression. FIG.20A FOXC1 and ERα mRNA levels in vector or FOXC1 overexpressing MCF-7cells were measured by RT-PCR (left) and real time RT-PCR (middle andright). FIG. 20B Protein levels of FOXC1, ERα, and ERα-regulated genesPR and IRS-1 in vector- or FOXC1-overexpressing MCF-7 cells weremeasured by immunoblotting. FIG. 20C FOXC1 was transiently transfectedinto MCF-7 cells. Immunofluorescence staining of FOXC1 (green) and ERα(red) was performed. The nuclear DNA (blue) was stained by DAPI.Magnification: ×400. FIG. 20D MCF-7 cells were transiently transfectedwith the ERE-luc reporter construct and the FOXC1 construct or thecontrol vector. Cells were treated with 10⁻⁸M 17β-estradiol (E2) for 24h, and were then lysed. Luciferase activity was measured and normalizedto β-galactosidase activity. Data represent mean±SD of three independentexperiments.

FIG. 21 is an immunoblot illustrating that FOXC1 downregulates ERαexpression. FOXC1 was stably transfected into T47D breast cancer cells.Protein levels of FOXC1, ERα, and ERα-regulated genes PR and IRS-1 invector- or FOXC1-overexpressing T47D cells were measured byimmunoblotting.

FIGS. 22A-22C show line (A) and bar (B-C) graphs illustrating that FOXC1reduces the sensitivity to estrogen and antiestrogen in breast cancercells. FIG. 22A Proliferation of FOXC1-overexpressing and control MCF-7cells in serum-free medium was measured by MTT assays. FIG. 22BFOXC1-overexpressing and control MCF-7 cells were serum-starved for 24h, and then treated with 10⁻⁸ M E2 for the indicated time periods. Cellproliferation was measured by MTT assays and is presented as relativegrowth rates compared with the vehicle control. FIG. 22CFOXC1-overexpressing and control MCF-7 cells in regular medium weretreated with 10⁻⁶ M tamoxifen for the indicated time periods. Cellproliferation was measured by MTT assays and is presented as relativegrowth rates vs. the vehicle control.

FIGS. 23A-23G show that FOXC1 induces NF-κB activity in breast cancercells. FIG. 23A Most significant canonical signaling pathways identifiedin the three breast cancer subgroups from the Richardson et al. datasetusing Ingenuity Pathway Analysis software is shown (Basal-like—(a),HER2—(b), Luminal—(c)). Genes from the dataset that were associated witha canonical pathway in the Ingenuity Pathways Knowledge Base wereconsidered for the analysis. Fischer's exact test was used to calculatea p-value determining the probability that the association between thegenes in a particular subgroup and the canonical pathway is explained bychance alone. Displayed canonical pathways appear in rank order of theirImpact Factor, the negative log of the Fischer's exact test p-value.FIG. 23 B Expression of NF-κB components in MCF-7 cells overexpressingFOXC1 or the vector was examined by immunoblotting. FIG. 23 C Expressionof p65 in 4T1 breast cancer cells stably transduced with control orFOXC1 shRNA was examined by immunoblotting. FIG. 23 D Nuclear proteinswere isolated from MCF-7 cells overexpressing FOXC1 or the controlvector, followed by immunoblotting of p65 and the nuclear protein LaminN C. FIG. 23 E Nuclear proteins were isolated from MCF-7 cellsoverexpressing FOXC1 or the control vector. The binding of p65, p50, andc-Rel to consensus DNA oligonucleotides was assessed by ELISA. Datarepresent mean±SD of three independent experiments. FIG. 23 F MCF-7cells were transiently transfected with NF-κB-luc, FOXC1, and asuper-repressor IκBα. NF-κB activity was assessed by luciferase assays.Each bar represents mean±SD of three independent experiments. FIG. 23 GMCF-7 cells overexpressing FOXC1 or the vector were treated with the IKKinhibitor BMS-345541 (5 μM). Cell proliferation at the indicated timepoints was measured by MTT assays and is presented as relative growthrates compared with the vehicle control.

FIGS. 24A-24D show that NF-κB downregulates ERα in breast cancer cells.FIG. 24A Expression of p65 and ERα in MCF-7 cells transfected with p65or the vector for 48 h was examined by immunoblotting. FIG. 24BExpression of ERα, PR, and IRS-1 in MCF-7 cells treated with the IKKinhibitor BMS-345541 (5 μM) for 24 h was examined by immunoblotting.FIG. 24C MCF-7 cells were transiently transfected with ERE-luc and ERαor p65, and then treated with 10⁻⁸M E2 for 24 h. ER activity wasassessed by luciferase assays. Each bar represents mean±SD of threeindependent experiments. FIG. 24D ChIP assays were performed asdescribed in Materials and Methods. Antibodies against p65 protein wereutilized to immunoprecipitate p65-DNA complexes. The input control was1% of the protein-chromatin supernatant subjected to ChIP assays. Theamplified ERα promoter region is −420/−280 (right).

FIG. 25 shows that NF-κB downregulates ERα in breast cancer cells.Expression of ERα in MCF-7 cells transfected with p65 or the vector for48 h was examined by real-time RT-PCR. Data represent mean±SD of threeindependent experiments.*, P<0.05 vs the vector control.

FIG. 26 shows representative immunostaining profiles of CK5/6, CK14 andFOXC1 in FFPE breast cancer specimens according to molecular subtype.

FIG. 27 shows the HRAS-transformed MCF10A cell series (M1-M4) geneexpression profiles and normal, basal, luminal A (lumA), luminal B(lumB), and Her2 molecular subtypes. M1-M4 gene expression profiles weremost reflective of the basal-like breast cancer molecular subtype.

FIGS. 28A-28B show the 3D culture RNA-Seq and 2D culture qRT-PCR resultsfor FOXC1 in HRAS-transformed MCF10A cell series (M1-M4). FIG. 28A showsthe 3D culture RNA-Seq results for FOXC1 and FIG. 28B shows the 2Dculture qRT-PCR results for FOXC1.

FIGS. 29A-29B show the 3D culture RNA-Seq and 2D culture qRT-PCR resultsfor FOXA1 in HRAS-transformed MCF10A cell series (M1-M4). FIG. 29A showsthe 3D culture RNA-Seq results for FOXA1 and FIG. 29B shows the 2Dculture qRT-PCR results for FOXA1.

FIGS. 30A-30H show the 2D culture qRT-PCR results for various genes inHRAS-transformed MCF10A cell series (M1-M4). FIG. 30A shows the 2Dculture qRT-PCR results for CDH1. FIG. 30B shows the 2D culture qRT-PCRresults for CDH2. FIG. 30C shows the 2D culture qRT-PCR results forFibronectin (FN1). FIG. 30D shows the 2D culture qRT-PCR results forVimentin (VIM). FIG. 30E shows the 2D culture qRT-PCR results forSERPINE1. FIG. 30F shows the 2D culture qRT-PCR results for MMP2. FIG.30G shows the 2D culture qRT-PCR results for SNAI2. FIG. 30H shows the2D culture qRT-PCR results for FOXC2.

FIG. 31 shows the 2D culture qRT-PCR results for mammospheres.

FIGS. 32A-32C show immunofluorescent images of FOXC1 counterstained withnuclear DAPI. FIG. 32A shows the immunofluorescent image of FOXC1counterstained with nuclear DAPI in M1, parental MCF10A cells. FIG. 32Bshows the immunofluorescent image of FOXC1 counterstained with nuclearDAPI in M2 H-RAS transformed MCF10A cells. FIG. 32C shows theimmunofluorescent image of FOXC1 counterstained with nuclear DAPI in M3tumorigenic and metastagenic cells.

FIGS. 33A-33C show mammosphere formation. FIG. 33A shows M1, parentalMCF10A cells. FIG. 33B shows mammosphere formation in M2 H-RAStransformed MCF10A cells. FIG. 33C shows mammosphere formation in M3tumorigenic and metastagenic cells.

FIG. 34 shows the ratio of FOXC1 to FOXA1 in all patients, the top 50%,top 20%, top 10%, and top 5% of patients with an elevated FOXC1/FOXA1expression ratio. According to one embodiment, these categoriescorrespond to a cutoff at the 50th percentile line, the 80th percentileline, the 90th percentile line, and the 95th percentile line,respectively.

FIG. 35 shows the ratio of FOXC1 to FOXA1 in all patients, the top 50%,top 20%, top 10%, and top 5% of patients with an elevated FOXC1/FOXA1expression ratio. According to one embodiment, these categoriescorrespond to a cutoff at the 50th percentile line, the 80th percentileline, the 90th percentile line, and the 95th percentile line,respectively.

FIG. 36 shows the ratio of FOXC1 to FOXA1 in lymph negative patients,including the bottom 80% and the top 20% of patients with an elevatedFOXC1/FOXA1 expression ratio. According to one embodiment, thesecategories correspond to a cutoff at the 80th percentile line.

FIG. 37 shows the Human FOXC1 Amino Acid Sequence (SEQ ID NO:1).

FIG. 38 shows the Human FOXA1 Amino Acid Sequence (SEQ ID NO:4).

FIG. 39 illustrates the regulation of CSCs.

FIG. 40 illustrates the parallel between mammary epitheliumdifferentiation and breast cancer subtypes.

FIG. 41 shows a proposed mechanism of FOXC1 regulation by Wnt/β cateninsignaling.

FIG. 42 shows FOXC1 as a downstream mediator of Wnt-driven regulation ofCSCs in basal-like breast cancer.

FIG. 43 shows an analysis of beta-catenin (CTNNB1) expression level andsurvival using a breast cancer transcriptomic database (Curtis et al.,2012).

FIGS. 44A-44D show the correlation between FOXC1 expression and Wntactivity upon biological and pharmacologic inhibition and stimulationwith a natural ligand. FIG. 44A shows FOXC1 expression upon biologicalinhibition with siRNA knockdown of LRP6, a canonical Wnt signaling cellsurface receptor. FIG. 44B shows the relative FOXC1 mRNA expression withthe siRNA knockdown of LRP6. FIG. 44C shows decreased FOXC1 expressionupon pharmacological inhibition with iCRT-3.

FIG. 44D shows an increase in FOXC1 expression with addition of Wnt3a, acanonical Wnt signaling ligand.

FIGS. 45A-45B show the relative survival of representative breast cancercell lines upon inhibition of Wnt/β catenin signaling with increasingdoses of the inhibitor iCRT3. FIG. 45A shows the relative survival ofthe MCF-7 (luminal) breast cancer cell line (see line with diamonds);the SKBR3 breast cancer cell line (overexpresses HER2) (see line withtriangles); and the BT-549 breast cancer cell line (basal-like) (seeline with squares). FIG. 45B shows the relative survival of the BT-549breast cancer cell line (high constitutive FOXC1 expression) (see linewith squares); the HS578T breast cancer cell line (high constitutiveFOXC1 expression) (see line with Xs); and the MDA-MB-231 breast cancercell line (low constitutive FOXC1 expression) (see line with circles).

FIGS. 46A-46B show the relative survival of representative breast cancercell lines upon proteasome inhibition with increasing doses of theinhibitor bortezomib. FIG. 46A shows the relative survival of the MCF-7(luminal) breast cancer cell line (see line with diamonds); the HS578Tbreast cancer cell line (basal-like) (see line with squares); and theSKBR3 breast cancer cell line (overexpresses HER2) (see line withtriangles). FIG. 46B shows the relative survival of the HS578T breastcancer cell line (basal-like) (bottom line); the BT-549 breast cancercell line (basal-like) (see middle line); and the MDA-MB-231 breastcancer cell line (low constitutive FOXC1 expression) (see top line).

FIGS. 47A-47B show mammosphere formation upon inhibition of Wnt/βcatenin signaling by iCRT-3. FIG. 47A shows mammosphere formation withaddition of DMSO and FIG. 47B shows mammosphere formation with theaddition of iCRT-3.

FIG. 48 is a bar graph showing the fold change of mRNA expression of thestem related genes BMI-1, FOXC1, KIT, NANOG, OCT4, and SOX2 uponinhibition of Wnt/β catenin signaling by addition of iCRT-3 on day 4(left hand bar) and day 7 (right hand bar).

FIGS. 49A-49B show mammosphere formation upon simultaneous inhibition ofWnt/β catenin signaling by addition of iCRT-3 and inhibition of NF-κBsignaling. FIG. 49A shows mammosphere formation with addition of DMSOand FIG. 49B shows mammosphere formation with the addition of iCRT-3 andbortezomib.

FIG. 50 shows the Human FOXC1 Amino Acid Sequence (SEQ ID NO:1).

DETAILED DESCRIPTION

The following description provides specific details for a thoroughunderstanding of, and enabling description for, embodiments of thedisclosure. However, one skilled in the art will understand that thedisclosure may be practiced without these details. In other instances,well-known structures and functions have not been shown or described indetail to avoid unnecessarily obscuring the description of theembodiments of the disclosure.

The embodiments described herein include methods for theranosticclassification of cancer, methods for treating breast cancer tumor cellsthat express FOXC1; and methods for diagnosing and treating metastaticbreast cancer based on an elevated expression ratio of FOXC1/FOXA1.

Theranostic Classification

A method for classifying a tumor using a theranostic biomarker withindependent prognostic significance is provided herein. A theranosticbiomarker provides information relevant to diagnosis, prognosis andtreatment of cancer in a subject. Although the present disclosurefocuses on methods related to breast cancer in humans, the methodsdescribed herein may be applied to any cancer having one or morebiomarkers with independent prognostic significance in any subjectsusceptible to developing breast cancer.

The term “theranostic biomarker” or a “theranostic classification” asused herein means a particular biomarker or classification that, inaddition to providing significant diagnostic and prognostic information,also provides information useful in optimizing treatment of a subjecthaving a disease such as cancer. The embodiments described hereinprovide a theranostic approach to classifying, diagnosing, prognosingand treating cancer. In practical terms, this means that a theranosticbiomarker or theranostic classification can identify which subjects andwhich tumors are most suited to a particular therapy, and also providesfeedback on the efficacy of a drug in order to demonstrate or determinehow well a drug should work or does work to optimize therapy or therapyregimens. It can also identify which subjects are resistant toparticular therapy or therapy regimens.

In one embodiment, the theranostic biomarker may be specific to adisease, such as breast cancer, or may be a general disease biomarker.In one embodiment, the theranostic biomarker is FOXC1. FOXC1 may be usedas an independent theranostic biomarker, or may be used in conjunctionwith other molecular biomarkers that are relevant to a particular typeof tumor or cancer. In one embodiment, FOXC1 may be used alone or inconjunction with estrogen receptor (ER), progesterone receptor (PR) andhuman epidermal growth factor receptor 2 (HER2; also known asErBb2mer-2/Neu) status for use in a method for theranosticclassification, diagnosis, prognosis and treatment breast cancer and itssubtypes. In some embodiments, such methods are useful in distinguishingbetween basal-like breast cancer subtypes, including hybrid basal-likebreast cancer subtypes that exhibit both basal-like breast cancercharacteristics and one or more characteristics of another subtype suchas luminal or HER2-enriched.

In one embodiment, the methods described herein include providing orobtaining a tumor tissue sample. The tumor tissue sample may be a freshfrozen tumor sample, a formalin-fixed paraffin-embedded (FFPE) sample, aprimary cell culture, or any other suitable tissue for determining anexpression level of a biomaker. In one embodiment, the tumor tissuesample is a breast cancer tumor tissue sample.

In some embodiments, an expression level of a theranostic biomarker suchas FOXC1 in a tumor tissue sample may be determined by qualitative orquantitative methods such as immunohistochemistry (IHC) orimmunocytochemistry (ICC), non-quantitative or quantitative reversetranscription polymerase chain reaction (RT-PCR or qRT-PCR), protein orcDNA microarray or by a QuantiGene® assay (Panomics). The expressionlevel may be a measurement of mRNA expression or protein expression.Data thus derived may be used to develop a cutoff expression level or anumerical prognostic index FOXC1 Score™ to aid in the clinicalprognostic stratification of specific subsets of patients with breastcancer (and/or other cancers including but not limited to, melanoma,neuroendocrine tumors, brain tumors such as glioblastoma multiforme,astrocytoma and other brain cancers, renal cell cancer, sarcomas (suchas synovial sarcoma) and leukemia. The numerical prognostic index FOXC1Score™ may be calculated from a standard curve as generated by plottingqRT-PCR values of FOXC1 mRNA expression against a specific clinicaloutcome measure such as overall survival (OS), breast-cancer specificsurvival, recurrence free survival, matastasis-free survival, othersuitable prognostic or outcome measures. The numerical prognostic indexFOXC1 Score™ may be used for determining a subject's prognosis and mayalso be used for clinical management purposes for tracking the efficacyor optimizing the efficacy of one or more therapy regimens.

Breast Cancer Subtype Molecular Classification

Molecular classification of breast cancer has identified specificsubtypes, often called “intrinsic” subtypes, with clinical andbiological implications, including an intrinsic luminal subtype, anintrinsic HER2-enriched subtype (also referred to as the HER2⁺ orER⁻/HER2⁺ subtype) and an intrinsic basal-like breast cancer (BLBC)subtype. (Perou et al. 2000). Identification of the intrinsic subtypeshas typically been accomplished by a combination of methods, including(1) histopathological detection, (2) ER, PR and HER2 expression statusand (3) detection of characteristic cellular markers.

Basal-like breast cancer, which expresses genes characteristic of basalepithelial cells in the normal mammary gland, comprises up to 15%-25% ofall breast cancers (Kreike et al. 2007) and is associated with the worstprognosis of all breast cancer types. BLBCs underexpress estrogenreceptor (ER⁻), progesterone receptor (PR⁻), and human epidermal growthfactor receptor 2 (HER2⁻) and encompass 60% to 90% of so-called“triple-negative” (ER⁻/PR⁻/HER2⁻) breast cancers. Although mostbasal-like breast cancers are often referred to as triple-negative basedon the expression status of ER, PR and HER2, not all basal-like breastcancers are triple negative. Thus, the intrinsic basal-like breastcancer subtype may be further subdivided into at least three distinctsubtypes described herein as “hybrid” basal-like breast cancer subtypes.In addition to a hybrid triple-negative subtype, the hybrid basal-likebreast cancer subtypes have profiles that resemble both basal-likebreast cancer and at least one other breast cancer molecular subtype.For example, hybrid basal-like subtypes can include a hybridbasal-like/HER2⁺ subtype that has a receptor profile of ER⁻/PR⁻/HER⁺, ahybrid basal-like/luminal subtype that has a receptor profile ofER⁺/PR^(− or +)/HER^(− or +), and a hybrid basal-like/triple negativesubtype that has a receptor profile of ER⁻/PR⁻/HER⁻. The existence andsignificance of these hybrid basal-like subtypes has not previously beenrecognized, but because they represent some of the most aggressive andresistant to treatment subtypes of breast cancer, the methods describedherein are important to improving the diagnosis, prognosis and treatmentof this disease. The term “basal-like breast cancers,” “basal-likesubtypes,” basal-like tumors,” “BLBCs” or the like as used herein ismeant to encompass all cancers and tumors that exhibit characteristicsof the BLBC subtype, including the intrinsic BLBC subtype, the hybridtriple-negative BLBC subtype, and any other hybrid basal-like subtypesdescribed herein that may display markers that are associated with theluminal, HER+ or other previously classified subtype.

The intrinsic HER2-enriched subtype (also described as the HER2⁺ orER⁻/HER2⁺ subtype) is characterized by underexpression of the hormonereceptors ER and PR and overexpression of HER2 (ER⁻/PR⁻/HER2⁺). TheHER2-enriched subtype is associated with a poor prognosis.

The intrinsic luminal breast cancer subtype is characterized byexpression or overexpression of ER and/or PR (ER⁺ and/or PR⁺). Theluminal subtype can be further subdivided based on HER2 status into theluminal A subtype, which is additionally characterized byunderexpression of HER2 (ER⁺/PR^(+ or −)/HER⁻), and luminal B subtype,which is additionally characterized by overexpression of HER2(ER⁺/PR^(+ or −)/HER⁺). Intrinsic luminal subtypes are often consideredto be the most treatable breast cancer subtype and are associated withthe best prognosis.

Whereas ER and HER2 guide treatment of luminal and HER2 breast cancers,respectively, chemotherapy remains the only modality of systemic therapyfor BLBC. Preferentially affecting younger women, particularly AfricanAmerican women, BLBCs are associated with high histologic grade,aggressive clinical behavior, and a high rate of metastasis to the brainand lung (Carey et al. 2006). Unlike other breast cancer subtypes, thereseems to be no correlation between tumor size and lymph node metastasisin BLBCs (Dent et al. 2007). Better understanding of the signalingpathways, biologic basis, and molecular mechanisms of basal-like,triple-negative breast cancer and other hybrid basal-like subtypesdescribed above allows identification of accurate biomarkers for earlydiagnosis, prognosis, and targeted therapy.

BLBCs are associated with expression of basal cytokeratins (CK5/6, CK14,and CK17), epidermal growth factor receptor (EGFR), c-kit, and p53 andassociated with the absence of ER, PR, and HER2 expression. With a largevariety of associated genes, BLBCs have been defined differently indifferent studies using a set of diagnostic markers. For example,Nielsen et al. defined BLBC on the basis of negative ER and negativeHER2 expression in addition to positive basal cytokeratin, EGFR, and/orc-kit expression (Nielsen et al. 2004). On the other hand, other groupshave defined BLBC on the basis of on a combination of negative ER, andnegative HER2 expression and positive CK5, P-cadherin, and p63expression (Elsheikh et al. 2008) or positive vimentin, EGFR, and CK5/6expression (Livasy et al. 2006). These different technical approaches incombination with widely varying patient cohorts may explain theinconsistent experimental results for these markers.

Identification of the basal-like subtype using immunohistochemistry(IHC) for detecting hormone receptors alone is less desirable thandetecting a theranostic biomarker, because identification is based onthe absence of IHC staining for estrogen receptor (ER), progesteronereceptor (PR), and human epidermal growth factor receptor 2 (HER2)rather than the presence of a specific tumor marker or markers. Itsdiagnosis is more one of exclusion rather than inclusion. Basal-likebreast cancer is often synonymously referred to as “triple negative”(i.e., ER⁻/PR⁻/HER2⁻), however, not all triple negative breast cancersare basal-like, and not all basal-like breast cancers are triplenegative. Although other molecular markers have been associated withbasal-like breast cancer as described above, such markers are notexclusive to this basal-like breast cancer and are therefore are notsuitable for use as stand-alone markers. The best hope for a realistic,potentially objective, and convenient method to identify basal-likecancers in clinical practices would be through the positive detection ofa definitive molecular marker or markers. Identification of FOXC1 as adominant regulator of the basal-like phenotype may provide a pragmaticapproach to distinguish this subgroup of breast cancer in clinicaldiagnosis, ultimately resulting in improved survival.

FOXC1 as a Biomarker for Basal-Like Breast Cancer

As described in the examples below, specific biomarkers for BLBC wereidentified and systemically analyzed using a 306-member intrinsic geneset (IGS) (Hu et al. 2006) in addition to other reported individualmarkers for BLBC using multiple microarray data sets. Degree ofcorrelation of each individual gene with the basal-like subtype based onmRNA expression was used to identify genes highly specific to BLBC. TheFOXC1 transcription factor emerged as a top-ranking gene. Therefore,diagnostic and prognostic significance of FOXC1 was assessed and therole of FOXC1 in regulating cellular functions in breast cancer wasfurther characterized.

Forkhead box transcription factors, including Forkhead box C1 (FOXC1,also known as forkhead-like 7 (FKHL7)), are transcription factorscharacterized by a common 100-amino acid winged-helix DNA-binding domaintermed the forkhead box domain, and play important roles in regulatingthe expression of genes involved in cell growth, survival,differentiation embryonic mesoderm development, migration, and longevity(Nishimura et al., 1998). The FOXC1/FKHL7 gene and protein sequences areknown, and can be found in GenBank (Accession Nos. AR140209 (completesequence; SEQ ID NO:16), AR140210 (coding sequence; SEQ ID NO:17) andAAE63616 (amino acid sequence; SEQ ID NO:18), the sequences of which areincorporated by reference in their entirety as if fully set forthherein). As a result of the studies described herein, it has beendetermined that FOXC1 expression in human breast cancer, both at themRNA and at the protein level, occurs consistently and exclusively inbasal-like breast cancers. Furthermore, in a head-to-head comparisonwith other suggested biomarkers of basal-like breast cancer and as shownby statistically significant in both univariate as well as multivariateanalyses described in the examples below, FOXC1 has emerged as the mostindicative and the most characteristic biomarker of BLBCs, in itsability to diagnose, prognose and treat BLBC.

One important feature of the above results was the exclusive nature ofthe association between FOXC1 and basal-like breast cancers: itsexpression is elevated only in basal-like molecular subtypes of breastcancers.

As mentioned above, while many genes are described to be characteristicbiomarkers of certain cancer types, and many others are described to beof functional importance to the survival and maintenance of themalignant phenotype, very few are demonstrated to have robust prognosticsignificance. This is because very few are critical by themselves andinstead are part of extremely large and complex networks of biomoleculeswhose overall function cannot be determined unless the molecules whichare most central and pivotal in the network are identified. FOXC1 hasbeen demonstrated to be of extremely high prognostic significance, beingpredictive of the high mortality and metastasis rate specificallyassociated with basal-like breast cancers.

Both basal-like triple-negative breast cancers as well as hybridbasal-like breast cancers (HER2 and luminal) have a high rate ofmetastasis to the brain, a devastating complication of this dreadeddisease. The studies described herein show that a 30-member genesignature associated with FOXC1 is predictive of the brain specificmetastases observed in the above two subtypes of breast cancer.

The clinical significance of FOXC1 expression is not restricted tobreast cancer but may extend to other cancers, including but not limitedto, melanoma, neuroendocrine tumors, brain tumors (such as glioblastomamultiforme, or astrocytoma), renal cell cancer, sarcomas (such assynovial sarcoma), and leukemia. FOXC1 expression has been shown tocharacteristically and exclusively define biologically and clinicallyaggressive subsets in such cancers and can be used both as a diagnosticas well as prognostic biomarker for these specific cancer types.Furthermore, similar to basal-like breast cancer, FOXC1 is a suitabletherapeutic target for these specific cancer types.

The above described findings have clear and important implications forpersonalized medicine and personalized cancer care as detection of FOXC1status of the described specific subsets of patients with breast cancer(and/or other cancers like gastric cancer and colon cancer) enables moretailored and specific therapeutic interventions with a greaterlikelihood of arresting disease progression, extending life expectancyor even achieving a cure.

In some embodiments, a method of use for a theranostic biomarker such asFOXC1 comprises an algorithm for its potential clinical use as adiagnostic tool. While FOXC1 may be used as an independent biomarker, itmay also be used alongside other biomarkers such as HER2, ER and PR. Forexample, in triple-negative breast cancer, the algorithm may include thefollowing steps. First, a patient who has either mammographic or breastMRI—detected abnormality or findings on a clinical examination isdesignated as suspicious for breast cancer. Next, the patient wouldundergo a FNNCore biopsy/Excisional biopsy to obtain a tumor tissue.Next, routine pathologic examination of the above-obtained tumor tissueestablishes diagnosis of breast cancer. The tumor tissue would then besubjected to immunohistochemical (IHC) staining for ER, PR and HER2.Patients that are found to be triple negative (i.e. ER⁻/PR⁻/HER2⁻) wouldhave their tumor tissue further tested by IHC for FOXC1. Next, patientsthat are found to be FOXC1 positive can thus be definitively diagnosedto have basal-like triple negative breast cancer.

In another embodiment a theranostic biomarker such as FOXC1 is used as aprognostic tool. FOXC1 may be used to predict the prognosis of factorsincluding, but not limited to, overall survival, recurrence-freesurvival, the propensity of developing a distant metastasis or the timeto develop a distant metastasis (such as brain metastasis), or apropensity for resistance to a targeted cancer therapy regimen. The term“propensity for resistance” to a targeted cancer therapy regimen as usedherein may be a predictor of resistance or a predictor of a decreasedefficacy (i.e., therapy is less effective from the start of treatment)of a targeted cancer therapy regimen in a cancer patient. A high levelof FOXC1 (either protein or RNA) predicts a poor prognosis of suchfactors, (i.e., decreased overall survival, decreased disease specificsurvival, decreased recurrence-free survival, increased rate ofloco-regional and for distant metastasis) as compared with low FOXC1levels in specific subsets of patients with breast cancer (and/or othercancers including but not limited to, melanoma, neuroendocrine tumors,brain tumors-such as glioblastoma multiforme, astrocytoma, renal cellcancer, sarcomas—such as synovial sarcoma, and leukemia).

In one embodiment, FOXC1's use as a prognostic tool includes analgorithm. For example, the algorithm may include the following steps,using triple-negative breast cancer as an example. First, a subjectwhose samples are qualitatively FOXC1 positive based on IHC have samplessent for further quantitative analysis for FOXC1 level using an RT-PCRor other quantitative technique such as a QuantiGene® assay (Panomics).Based on the quantitative value of FOXC1 expression thus obtained, anumerical Prognostic Index FOXC1 Score™ will be calculated for theindividual patient which will help determine patient-specific estimatesof overall survival, recurrence free survival, time to distantmetastasis and type of metastasis associated with basal-liketriple-negative breast cancer. This method makes personalized medicalcare possible for BLBC patients.

FOXC1 Represses Estrogen Receptor-α Expression in Human Breast cancerCells by Increasing Nuclear Factor-κB (NF-κB) Signaling

The sex steroid hormone estrogen plays important roles in thedevelopment of normal mammary glands and breast cancer (Dhasarathy etal., 2007). Most established effects of estrogen are mediated throughits direct binding to two nuclear receptors, estrogen receptor (ER)-αand -β (Couse and Korach, 1999; Kuiper et al., 1997). Both receptors aretranscription factors that induce the expression of many breastcancer-related genes. Although ERβ is expressed in breast cancer, itsrole in tumor progression is not clear (Fuqua et al., 2003). On theother hand, the role of ERα in human breast cancer is well-established.More than 60% of human breast cancers are ERα positive (Keen andDavidson, 2003). It is a prognostic factor for breast cancer andcorrelates with a higher degree of tumor differentiation and increaseddisease-free survival (Osborne, 1998). Thus ERα expression defines asubgroup of breast cancer patients who, in general, have a morefavorable prognosis than patients with ERα-negative tumors (Zhao et al.,2008). ERα is also a target for antiestrogen therapy and a predictivemarker for response to the therapy (Park and Jordan, 2002).

There is tremendous interest in understanding the mechanisms whereby ERαexpression and signaling is modulated in breast cancer and in exploitingthis knowledge to develop and improve therapeutic interventionstargeting ERα. Although several transcription factors or signalingproteins have been identified as ERα regulators, the cellular andmolecular events that regulate ERα expression in tumors are not wellunderstood as yet. In addition, the clinical relevance and biologicalsignificance of these regulations are still under investigation. It wasfound that p53 binds to the ERα promoter and positively regulates thetranscription of ERα in breast cancer cells (Shirley et al., 2009). Incontrast, another study showed that p53 activation decreases thetranscriptional activity of ERα by elevating the Kruppel-like factor 4transcription factor, which can interfere with the DNA-binding functionof ERα (Akaogi et al., 2009). Similarly, the BRCA1 tumor suppressor genehas been found to activate or inhibit ERα expression in differentstudies (Hosey et al., 2007; Rosen et al., 2005). The transcriptionfactor Oct-1 can also be recruited to the ERα promoter to elicit ERαtranscription (Hosey et al., 2007).

In breast cancer cell lines, expression of ERα is associated with levelsof active forkhead box O protein 3a (FOXO3a) (Guo and Sonenshein, 2004).Increased FOXO3a expression induces ERα transcription and proteinlevels. FOXO3a can bind to two conserved forkhead binding sites in theERα promoter. Thus FOXO3a may represent an important intracellularmediator of ERα expression (Guo and Sonenshein, 2004). In support ofthis study, Belguise et al. showed that PKCq is elevated in ERα-negativebreast cancers, activates Akt and thereby inactivates FOXO3a, leading todecreased synthesis of ERα (Belguise and Sonenshein, 2007). It is alsowell-documented that hyperactivation of MAPK induces loss of ERαexpression in breast cancer cells (Oh et al., 2001). Both Akt and MAPKmay be implicated in the downregulation of ERα by EGFR/HER-2, which maygive rise to an inverse correlation between EGFR/HER-2 and ERα status inbreast cancers (Oh et al., 2001; Saceda et al., 1996). Most recently, aG protein-coupled receptor Adenosine A1 receptor has been reported toupregulate ERα expression (Lin et al.). Furthermore, ERα expression canalso be regulated through epigenetic modification, e.g. hypermethylationat its promoter, which has been reported to be responsible for the lossof ERα in some breast cancer cells (Yoshida et al., 2000).

As described by the studies herein, forkhead box transcription factorFOXC1 has been identified as an important marker for human basal-likebreast cancer, which lacks or under-expresses estrogen receptor-α (ERα).Further, as discussed in detail below, FOXC1 expression was shown toconsistently and inversely correlate with ERα expression by analyzingmultiple cDNA microarray data sets of human breast cancer.Overexpression of FOXC1 in ERα-positive breast cancer cellsdownregulated ERα mRNA and protein levels, and reduced cellularresponses to estradiol and tamoxifen treatment. FOXC1 overexpressioncaused an increase in levels of p65 protein, thereby elicitingNF-κB-mediated suppression of ERα. Pharmacologic inhibition of NF-κB inFOXC1-overexpressing MCF-7 breast cancer cells diminished these effectsof FOXC1. Taken together, these results reveal a FOXC1-driven mechanismthat explains the loss or low expression of ERα in basal-like breastcancer and provide a paradigm for studying the regulation of ERα duringbreast cancer progression.

FOXC1 as a Therapeutic Target for Basal-Like Breast Cancer

The studies described herein show that FOXC1 plays an important role ininitiating and maintaining the aggressive capacity for cellularproliferation, invasion and migration that is typical of basal-likebreast cancers. These are well accepted precursor attributes that arenecessary for and associated with metastasis to distant organs, aclinical feature which is predicted by a patient's FOXC1 status.

Studies in which FOXC1 expression is targeted and knocked downdramatically reduces the above aggressive features of cancer cells. Thisdemonstrates the utility of FOXC1 as a therapeutic drug targetspecifically for basal-like breast cancers.

While the clinical significance of the hybrid basal-like subtypesdescribed above has not previously been recognized, these subtypes aretypically resistant to targeted receptor therapy, even though theyexpress the target receptor. For example, the hybrid basal-like/HER2⁺subtype is typically intrinsically resistant to HER2⁺ targeted therpiesincluding, but not limited to, anti-HER2 antibodies (e.g., trastuzumab(Herceptin®), pertuzumab and ertumaxomab) and tyrosine kinase inhibitors(e.g., lapatinib)), despite being HER2 positive. Similarly, the hybridbasal-like/luminal subtype is typically intrinsically resistant tohormone receptor targeted therapies including, but not limited to,selective estrogen receptor modulators (SERMS) (e.g., tamoxifen), andother therapies such as aromatase inhibitors (e.g., anastozole(Arimidex®), exemestane (Aromasin®) and letrozole (Femara®)) andanti-estrogens (e.g., toremifene citrate (Fareston®), This resistance toor decrease in efficacy to targeted receptor therapy is indicated byFOXC1. Thus, FOXC1 positive status may be used as a predictive biomarkerof resistance to or decrease in efficacy of biologic therapy attemptedwith trastuzumab (Herceptin®) or tamoxifen in patients with hybridbasal-like breast cancers. Administration of targeted therapy directedagainst FOXC1 in hybrid basal-like/HER2⁺subtype tumors and hybridbasal-like/luminal subtype tumors should restore therapeutic sensitivityto trastuzumab (Herceptin®) and tamoxifen, respectively.

Hybrid basal-like subtype tumors are even more aggressive in theirbiology and clinical characteristics than either the molecular subtype(HER2⁺ or luminal) or the basal-like subtype alone. Hence any and alltherapeutic efforts in this group should include FOXC1 targeted therapyas well as targeted therapy from the earliest possible time afterdiagnosis.

Validated as a prognostic biomarker, FOXC1 status can be utilized inclinical decision making with respect to recommendations for offeringstandard adjuvant chemotherapy, enrollment in adjuvant chemotherapyclinical trials, offering neoadjuvant chemotherapy, and enrollment inneoadjuvant chemotherapy clinical trials, to patients with basal-likebreast cancer. FOXC1 status may also be utilized in clinical decisionmaking with respect to treatment recommendations for a triplenegative-diagnosed patient based on a determination that the patient hasa BLBC subtype that is resistant to targeted or other treatments ortreatment regimens. For example, a patient diagnosed as triple negativeand FOXC1⁺ is likely to be resistant to most targeted therapies and/orchemotherapy, and may therefore decide to forego treatments or treatmentregimens in favor of living the rest of their life without the negativeeffects that are often associated with said treatments. Alternatively, aFOXC1 inhibitor or other FOXC1 targeted therapy may be used inconjunction with adjuvant and neoadjuvant chemotherapy regimens.

The pharmaceutical composition may include, but is not limited to, anFKBP52 inhibitor, a CD147 inhibitor, and a pharmaceutically acceptablecarrier.

In one embodiment, a method for treating cancer may includeadministering a pharmaceutical composition that includes apharmaceutically acceptable carrier and a therapeutically effectiveamount of a substance that targets and inhibits FOXC1 expression oractivity (a FOXC1 inhibitor) for the targeted biologic therapy ofbasal-like/triple negative breast cancer. In another embodiment, thepharmaceutical composition may also include a therapeutically effectiveamount of a substance that targets a receptor for the targeted biologictherapy of other hybrid basal-like breast cancers. In one embodiment,the substance that targets a receptor may include, but is not limitedto, ER (for targeting the hybrid basal-like/luminal subtype) or HER2(for targeting the hybrid basal-like/HER2 subtype).

In one embodiment, the FOXC1 inhibitor may include any suitablesubstance able to target intracellular proteins or nucleic acidmolecules alone or in combination with an appropriate carrier orvehicle, including, but not limited to, an antibody or functionalfragment thereof, (e.g., Fab′, F(ab′)2, Fab, Fv, rIgG, and scFvfragments and genetically engineered or otherwise modified forms ofimmunoglobulins such as intrabodies and chimeric antibodies), smallmolecule inhibitors of the FOXC1 protein, chimeric proteins or peptides,gene therapy for inhibition of FOXC1 transcription, or an RNAinterference (RNAi)-related molecule or morpholino molecule able toinhibit FOXC1 gene expression and/or translation. In one embodiment theFOXC1 inhibitor is an RNAi-related molecule such as an siRNA or an shRNAfor inhibition of FOXC1 translation. An RNA interference (RNAi) moleculeis a small nucleic acid molecule, such as a short interfering RNA(siRNA), a double-stranded RNA (dsRNA), a micro-RNA (miRNA), or a shorthairpin RNA (shRNA) molecule, that complementarily binds to a portion ofa target gene or mRNA so as to provide for decreased levels ofexpression of the target.

The pharmaceutical compositions of the subject invention can beformulated according to known methods for preparing pharmaceuticallyuseful compositions. Furthermore, as used herein, the phrase“pharmaceutically acceptable carrier” means any of the standardpharmaceutically acceptable carriers. The pharmaceutically acceptablecarrier can include diluents, adjuvants, and vehicles, as well asimplant carriers, and inert, non-toxic solid or liquid fillers,diluents, or encapsulating material that does not react with the activeingredients of the invention. Examples include, but are not limited to,phosphate buffered saline, physiological saline, water, and emulsions,such as oil/water emulsions. The carrier can be a solvent or dispersingmedium containing, for example, ethanol, polyol (for example, glycerol,propylene glycol, liquid polyethylene glycol, and the like), suitablemixtures thereof, and vegetable oils. In one embodiment, thepharmaceutically acceptable carrier is a PEGylated immunoliposome forencapsulating the RNAi-related molecule. The PEGylated immunoliposomesor other carrier or delivery vehicle may be specifically targeted tobasal-like tumor cells or specific hybrid basal-like subtype tumor cellsby conjugating recombinant human and/or chimeric monoclonal antibodiesor functional fragments thereof to the liposomal membrane which arespecific for cell surface protein and/or carbohydrate and/orglycoprotein markers specific to the basal-like subtype that istargeted. Such markers that may be targteted include, but are notlimited to, CD109, HMW-MAA, HER2, ER, CK5/6, EGFR, c-Kit and any othersuitable marker for targeting a desired tumor subtype.

Compositions containing pharmaceutically acceptable carriers aredescribed in a number of sources which are well known and readilyavailable to those skilled in the art. For example, Remington: TheScience and Practice of Pharmacy (Gerbino, P. P. [2005] Philadelphia,Pa., Lippincott Williams & Wilkins, 21st ed.) describes formulationsthat can be used in connection with the subject invention. Formulationssuitable for parenteral administration include, for example, aqueoussterile injection solutions, which may contain antioxidants, buffers,bacteriostats, and solutes which render the formulation isotonic withthe blood of the intended recipient; and aqueous and nonaqueous sterilesuspensions which may include suspending agents and thickening agents.The formulations may be presented in unit-dose or multi-dose containers,for example sealed ampoules and vials, and may be stored in a freezedried (lyophilized) condition requiring only the condition of thesterile liquid carrier, for example, water for injections, prior to use.Extemporaneous injection solutions and suspensions may be prepared fromsterile powder, granules, tablets, etc. It should be understood that inaddition to the ingredients particularly mentioned above, theformulations of the subject invention can include other agentsconventional in the art having regard to the type of formulation inquestion.

The pharmaceutical composition described above is administered and dosedin accordance with good medical practice, taking into account theclinical condition of the individual patient, the site and method ofadministration, scheduling of administration, patient age, sex, bodyweight, and other factors known to medical practitioners. Thetherapeutically effective amount for purposes herein is thus determinedby such considerations as are known in the art. For example, aneffective amount of the pharmaceutical composition is that amountnecessary to provide a therapeutically effective decrease in FOXC1. Theamount of the pharmaceutical composition should be effective to achieveimprovement including but not limited to total prevention and toimproved survival rate or more rapid recovery, or improvement orelimination of symptoms associated with the chronic inflammatoryconditions being treated and other indicators as are selected asappropriate measures by those skilled in the art. In accordance with thepresent invention, a suitable single dose size is a dose that is capableof preventing or alleviating (reducing or eliminating) a symptom in apatient when administered one or more times over a suitable time period.One of skill in the art can readily determine appropriate single dosesizes for systemic administration based on the size of the patient andthe route of administration.

FOXC1 Expression and Treatment of Basal-Like and Claudin-Low BreastCancer

The Wnt/β catenin signaling pathway is well known as a regulator ofembryonic development and stem cell biology, and is prominently activein basal-like and claudin-low breast cancers. As described in detailherein, transcription factor FOXC1 plays a role in mediating aggressivecell traits in basal-like/claudin low breast cancer (see Ray et al.,2010). As shown in Example 1 below, FOXC1 is a target of Wnt/β cateninsignaling and FOXC1 expression predicts response to Wnt inhibition.Further, Wnt mediated regulation of FOXC1 appears to be critical forcancer stem cells (CSCs) in basal-like/claudin low breast cancer.

However, Wnt inhibitors as a single agent may not work well in treatingbasal-like/claudin low breast cancer. As such, treatment cocktailstargeting multiple pathways governing CSCs need to be considered forrational design of effective targeted therapy against basal-like/claudinlow breast cancer. As discussed herein, bortezomib, a proteasomeinhibitor that inhibits nuclear factor kappa-light-chain-enhancer ofactivated B cells (NF-κB), effectively overcomes cancer stem cell escapetriggered by Wnt inhibitor therapy in Forkhead box C1 (FOXC1) positivebasal-like/claudin-low breast cancer. This makes proteasome inhibitorssuch as bortezomib a candidate drug to treat all types ofbasal-like/claudin-low breast cancers. Bortezomib has previously beenused in a clinical trial to treat triple-negative patients; however,this trial yielded inconclusive results because it was not possible toaccurately select basal-like patients. Thus, the patients in the trialwere composed of a mix of basal-like positive and basal-like negativepatients. FOXC1 is a marker for basal-like breast cancer, which affects˜70% of triple negative breast cancer. Further, basal-like breast cancerincludes more than just triple-negative breast cancer as it is presentin up to 30% ER+ type cancers and in >15% HER2+ type cancers. Screeningof patients using FOXC1 as a marker will help to more efficiently selectpatients with basal-like positive cancers for treatment with bortezomib.

Forkhead box transcription factors, including FOXC1, also known asforkhead-like 7 (FKHL7)), are transcription factors characterized by acommon 100-amino acid winged-helix DNA-binding domain termed theforkhead box domain, and play important roles in regulating theexpression of genes involved in cell growth, survival, differentiationembryonic mesoderm development, migration, and longevity (Nishimura etal., 1998). The FOXC1/FKHL7 gene and protein sequences are known, andcan be found in GenBank (Accession Nos. AR140209 (complete sequence)(SEQ ID NO:16), AR140210 (coding sequence) (SEQ ID NO:17) and AAE63616(amino acid sequence) (SEQ ID NO:18). As a result of the studiesdescribed in International Patent Application No. PCT/US 10/44817, filedAug. 6, 2010, and entitled “Methods for Diagnosis, Prognosis, andTreatment of Primary and Metastatic Basal-Like Breast Cancer and OtherCancer Types,” filed Feb. 3, 2012, it was determined that FOXC1expression in human breast cancer, both at the mRNA and at the proteinlevel, occurs consistently and exclusively in basal-like breast cancers.It was shown that FOXC1 is elevated only in basal-like molecularsubtypes of breast cancers, and has been demonstrated to be of highprognostic significance, as it is predictive of the high mortality andmetastasis rate specifically associated with basal-like breast cancers.Furthermore, in a head-to-head comparison with other suggestedbiomarkers of basal-like breast cancer and as shown by statisticallysignificant in both univariate as well as multivariate analyses, FOXC1has emerged as the most indicative and the most characteristic biomarkerof basal-like breast cancers, in its ability to diagnose, prognose, andtreat basal-like breast cancers.

Provided herein are methods for treating a subject with breast cancerincluding first screening a population of breast cancer tumor cells ofthe subject to determine whether the breast cancer tumor cells expressFOXC1, and subsequently treating that subject having breast cancer tumorcells expressing FOXC1 with bortezomib. In certain embodiments, thebreast cancer tumor cells express FOXC1 if the level of FOXC1 is higherthan a predetermined cutoff level. In certain embodiments, if the levelof FOXC1 is higher than a predetermined cutoff level, then the subjectmay be classified as having basal-like breast cancer. In certainembodiments, the predetermined cutoff level may be determined by a 90thpercentile level of FOXC1 expression levels for a dataset of breastcancer tumors, the dataset comprising all breast cancer subtypes.

Methods used to determine whether a population of tumor cells expressFOXC1 may include any suitable method, including but not limited to,immunohistochemistry (or other immunoassay), PCR, RT-PCR, quantitativeRT-PCR (qRT-PCR) (or any other PCR-based method), and/or the methods,assays and materials described in International Application Nos.PCT/US10/44817 entitled “Methods for Diagnosis, Prognosis, and Treatmentof Primary and Metastatic Basal-Like Breast Cancer and Other CancerTypes;” and PCT/US12/23871 entitled “FOXC1 Antibodies and Methods ofTheir Use;” the subject matter of both of which are hereby incorporatedby reference as if fully set forth herein.

In certain embodiments, detecting the presence or absence of FOXC1 maybe accomplished by an in vitro immunoassay, such as immunocytochemistry(ICC), immunohistochemistry (IHC), Western blot or fluorescent in situhybridization (FISH). Alternatively, an in vivo imaging modality may beused, such as magnetic resonance imaging (MRI), positron emissiontomography (PET) or microPET, computed tomography (CT), PET/CTcombination imager, cooled charged coupled device (CCD), camera opticalimaging, optical imaging and single photon emission computed tomography(SPECT). When the presence or absence of FOXC1 is determined by an invivo method, the FOXC1 antibody or functional fragment thereof should beconjugated to an intracellular delivery agent to facilitate deliver ofthe antibody or functional fragment thereof to the cytoplasm of targetcells.

In certain embodiments, an anti-FOXC1 monoclonal antibody may be used todetect expression level of FOXC1 in a cell. As described in detail inPCT/US12/23871, the antibody may specifically bind a target antigenicpeptide sequence of human FOXC1 (FIG. 50; SEQ ID NO:1). In one aspect,the target antigenic peptide sequence is 5′-AHAEQYPGGMARAYGPYTPQPQPKD-3′(SEQ ID NO:2), which corresponds to amino acids 51 to 75 of SEQ ID NO:1(see FIG. 50). A cysteine residue may be added to the N-terminus (i.e.,5′-C-AHAEQYPGGMARAYGPYTPQPQPKD-3′ (SEQ ID NO:3)) to assist inconjugation to the carrier protein as necessary.

In certain embodiments, if the tumor cells express FOXC1, the subjectmay be administered one or more proteasome inhibitors, and optionallyone or more Wnt inhibitors, to treat the cancer. In certain embodiments,the proteasome inhibitor may be bortezomib. In certain embodiments, theWnt inhibitor may be beta-Catenin/Tcf Inhibitor III (i.e., iCRT3 or2-[[[2-(4-ethylphenyl)-5-methyl-4-oxazolyl]methyl]thio]-N-(2-phenylethyl)acetamide).In certain embodiments, the cancer may be basal-like/claudin-low breastcancer. In certain embodiments, the cancer may be basal-like breastcancer. For example, the basal-like breast cancer may be a triplenegative breast cancer, an ER positive breast cancer, or a HER2 positivebreast cancer. In certain embodiments, the cancer may be claudin-lowbreast cancer.

Also provided herein are methods for treating cancer in a subject. Incertain embodiments, the methods for treating a cancer in a subjectinclude determining the expression level of FOXC1 in a population oftumor cells obtained from the subject; and administering one or moreproteasome inhibitors, and optionally one or more Wnt inhibitors, to thesubject to treat the cancer if the population of tumor cells expressesFOXC1. In certain embodiments, the proteasome inhibitor may bebortezomib. In certain embodiments, the cancer may bebasal-like/claudin-low breast cancer. In certain embodiments, the cancermay be basal-like breast cancer. For example, the basal-like breastcancer may be triple negative breast cancer, ER positive breast cancer,or HER2 positive breast cancer. In certain embodiments, the cancer maybe claudin-low breast cancer.

Also provided herein are combination therapies comprising one or moreWnt inhibitors and one or more proteasome inhibitors. In certainembodiments, the proteasome inhibitor may be bortezomib. In certainembodiments, the one or more Wnt inhibitors may be iCRT3. In certainembodiments, the combination therapy may be used to treat a FOXC1positive cancer. In certain embodiments, the FOXC1 positive cancer maybe a basal-like breast cancer. For example, the basal-like breast cancermay be a triple negative breast cancer, an ER positive breast cancer, ora HER2 positive breast cancer. In certain embodiments, the cancer may beclaudin-low breast cancer.

Optimal dosages of the combination therapy (i.e., the one or more Wntinhibitors and one or more proteasome inhibitors) to be administered maybe determined by those skilled in the art, and will vary with theparticular Wnt inhibitor and/or proteasome inhibitor used, the strengthof the preparation, the mode of administration, and the advancement ofthe disease condition. Additional factors depending on the particularsubject being treated, include, without limitation, subject age, weight,gender, diet, time of administration, time and frequency ofadministration, drug combination(s), reaction sensitivities, andresponse to therapy. Administration of the pharmaceutical compositionmay be effected continuously or intermittently. In any treatmentregimen, the pharmaceutical composition may be administered to a subjecteither singly or in a cocktail containing one or more Wnt inhibitors andone or more proteasome inhibitors, other therapeutic agents,compositions, or the like, including, but not limited to,tolerance-inducing agents, potentiators and side-effect relievingagents. All of these agents are administered in generally-acceptedefficacious dose ranges such as those disclosed in the Physician's DeskReference, 41 st Ed., Publisher Edward R. Barnhart, N.J. (1987), whichis herein incorporated by reference as if fully set forth herein. [0030]“Treating” or “treatment” of a condition may refer to preventing thecondition, slowing the onset or rate of development of the condition,reducing the risk of developing the condition, preventing or delayingthe development of symptoms associated with the condition, reducing orending symptoms associated with the condition, generating a complete orpartial regression of the condition, or some combination thereof.Treatment may also mean a prophylactic or preventative treatment of acondition.

In certain aspects of the embodiments described above, the use of aproteasome inhibitor (e.g., bortezomib) and optionally, one or more Wntinhibitors, for treating a FOXC1 positive cancer is provided herein. Incertain embodiments, the FOXC1 positive cancer may be a basal-likebreast cancer. For example, the basal-like breast cancer may be a triplenegative breast cancer, an ER positive breast cancer, or a HER2 positivebreast cancer. In certain embodiments, the cancer may be claudin-lowbreast cancer. All of the embodiments described herein apply to suchuses.

As used herein, a “subject” refers to a mammal, such as a human. In someembodiments, the subject is a patient.

In certain embodiments, the one or more proteasome inhibitors, andoptionally, one or more Wnt inhibitors, may be administered incombination with a therapeutic agent, radiotherapy, surgery, or anycombination thereof.

As used herein, a “therapeutically effective amount,” “therapeuticallyeffective concentration” or “therapeutically effective dose” is anamount which, as compared to a corresponding subject who has notreceived such amount, results in improved treatment, healing,prevention, or amelioration of a disease, disorder, or side effect, or adecrease in the rate of advancement of a disease or disorder.

This amount will vary depending upon a variety of factors, including butnot limited to the characteristics of the one or more Wnt inhibitors andone or more proteasome inhibitors or pharmaceutical compositions thereof(including activity, pharmacokinetics, pharmacodynamics, andbioavailability thereof), the physiological condition of the subjecttreated (including age, sex, disease type and stage, general physicalcondition, responsiveness to a given dosage, and type of medication) orcells, the nature of the pharmaceutically acceptable carrier or carriersin the formulation, and the route of administration. Further, aneffective or therapeutically effective amount may vary depending onwhether the one or more Wnt inhibitors and one or more proteasomeinhibitors disclosed herein or the pharmaceutical composition thereof isadministered alone or in combination with other drug(s), othertherapy/therapies or other therapeutic method(s) or modality/modalities.One skilled in the clinical and pharmacological arts will be able todetermine an effective amount or therapeutically effective amountthrough routine experimentation, namely by monitoring a cell's orsubject's response to administration of the one or more Wnt inhibitorsand one or more proteasome inhibitors or the pharmaceutical compositionthereof and adjusting the dosage accordingly. For additional guidance,see Remington: The Science and Practice of Pharmacy, 21 st Edition,Univ. of Sciences in Philadelphia (USIP), Lippincott Williams & Wilkins,Philadelphia, Pa., 2005, which is hereby incorporated by reference as iffully set forth herein for additional guidance for determining atherapeutically effective amount.

In some embodiments, the one or more proteasome inhibitors, andoptionally, one or more Wnt inhibitors, used in the methods andtherapies herein may be administered as a combination of one or moretherapeutic agents for the treatment of cancer. “A combination” or “incombination with,” as used herein, means in the course of treating thesame cancer in the same subject using two or more agents, drugs,treatment regimens, treatment modalities or a combination thereof, inany order. This includes simultaneous administration, as well as in atemporally spaced order of up to several days apart. Such combinationtreatment may also include more than a single administration of any oneor more of the agents, drugs, treatment regimens or treatmentmodalities. Further, the administration of the two or more agents,drugs, treatment regimens, treatment modalities or a combination thereofmay be by the same or different routes of administration.

The treatment as described herein may be administered by any suitableroute of administration, alone or as part of a pharmaceuticalcomposition. A route of administration may refer to any administrationpathway known in the art, including but not limited to aerosol, enteral,nasal, ophthalmic, oral, parenteral, rectal, transdermal (e.g., topicalcream or ointment, patch), or vaginal. “Transdermal” administration maybe accomplished using a topical cream or ointment or by means of atransdermal patch. “Parenteral” refers to a route of administration thatis generally associated with injection, including infraorbital,infusion, intraarterial, intracapsular, intracardiac, intradermal,intramuscular, intraperitoneal, intrapulmonary, intraspinal,intrastemal, intrathecal, intrauterine, intravenous, subarachnoid,subcapsular, subcutaneous, transmucosal, or transtracheal.

Examples of therapeutic agents that may be administered as a treatmentinclude, but are not limited to, chemotherapeutic agents, therapeuticantibodies and fragments thereof, toxins, radioisotopes, enzymes (e.g.,enzymes to cleave prodrugs to a cytotoxic agent at the site of thetumor), nucleases, hormones, immunomodulators, antisenseoligonucleotides, nucleic acid molecules (e.g., mRNA molecules, cDNAmolecules or RNAi molecules such as siRNA or shRNA), chelators, boroncompounds, photoactive agents and dyes. The therapeutic agent may alsoinclude a metal, metal alloy, intermetallic or core-shell nanoparticlebound to a chelator that acts as a radiosensitizer to render thetargeted cells more sensitive to radiation therapy as compared tohealthy cells.

Chemotherapeutic agents that may be used in accordance with theembodiments described herein are often cytotoxic or cytostatic in natureand may include, but are not limited to, alkylating agents,antimetabolites, anti-tumor antibiotics, topoisomerase inhibitors,mitotic inhibitors hormone therapy, targeted therapeutics andimmunotherapeutics. In some embodiments the chemotherapeutic agents thatmay be used as therapeutic agents in accordance with the embodiments ofthe disclosure include, but are not limited to, 13-cis-Retinoic Acid,2-Chlorodeoxyadenosine, 5-Azacitidine, 5-Fluorouracil, 6-Mercaptopurine,6-Thioguanine, actinomycin-D, adriamycin, aldesleukin, alemtuzumab,alitretinoin, all-transretinoic acid, alpha interferon, altretamine,amethopterin, amifostine, anagrelide, anastrozole, arabinosylcytosine,arsenic trioxide, amsacrine, aminocamptothecin, aminoglutethimide,asparaginase, azacytidine, bacillus calmette-guerin (BCG), bendamustine,bevacizumab, bexarotene, bicalutamide, bleomycin, busulfan, calciumleucovorin, citrovorum factor, capecitabine, canertinib, carboplatin,carmustine, cetuximab, chlorambucil, cisplatin, cladribine, cortisone,cyclophosphamide, cytarabine, darbepoetin alfa, dasatinib, daunomycin,decitabine, denileukin diftitox, dexamethasone, dexasone, dexrazoxane,dactinomycin, daunorubicin, decarbazine, docetaxel, doxorubicin,doxifluridine, eniluracil, epirubicin, epoetin alfa, erlotinib,everolimus, exemestane, estramustine, etoposide, filgrastim,fluoxymesterone, fulvestrant, flavopiridol, floxuridine, fludarabine,fluorouracil, flutamide, gefitinib, gemcitabine, gemtuzumab ozogamicin,goserelin, granulocyte—colony stimulating factor, granulocytemacrophage-colony stimulating factor, hexamethylmelamine, hydrocortisonehydroxyurea, ibritumomab, interferon alpha, interleukin-2,interleukin-11, isotretinoin, ixabepilone, idarubicin, imatinibmesylate, ifosfamide, irinotecan, lapatinib, lenalidomide, letrozole,leucovorin, leuprolide, liposomal Ara-C, lomustine, mechlorethamine,megestrol, melphalan, mercaptopurine, mesna, methotrexate,methylprednisolone, mitomycin C, mitotane, mitoxantrone, nelarabine,nilutamide, octreotide, oprelvekin, oxaliplatin, paclitaxel,palbociclib, pamidronate, pemetrexed, panitumumab, PEG Interferon,pegaspargase, pegfilgrastim, PEG-L-asparaginase, pentostatin,plicamycin, prednisolone, prednisone, procarbazine, raloxifene,rituximab, romiplostim, ralitrexed, sapacitabine, sargramostim,satraplatin, sorafenib, sunitinib, semustine, streptozocin, tamoxifen,tegafur, tegafur-uracil, temsirolimus, temozolamide, teniposide,thalidomide, thioguanine, thiotepa, topotecan, toremifene, tositumomab,trastuzumab, tretinoin, trimitrexate, alrubicin, vincristine,vinblastine, vindestine, vinorelbine, vorinostat, or zoledronic acid.

Therapeutic antibodies and functional fragments thereof, that may beused as therapeutic agents in accordance with the embodiments of thedisclosure include, but are not limited to, alemtuzumab, bevacizumab,cetuximab, edrecolomab, gemtuzumab, ibritumomab tiuxetan, panitumumab,rituximab, tositumomab, and trastuzumab and other antibodies associatedwith breast cancer.

Toxins that may be used as therapeutic agents in accordance with theembodiments of the disclosure include, but are not limited to, ricin,abrin, ribonuclease (RNase), DNase I, Staphylococcal enterotoxin-A,pokeweed antiviral protein, gelonin, diphtheria toxin, Pseudomonasexotoxin, and Pseudomonas endotoxin.

Radioisotopes that may be used as therapeutic agents in accordance withthe embodiments of the disclosure include, but are not limited to, ³²P,⁸⁹Sr, ⁹⁰Y, ^(99m)Tc, ⁹⁹Mo, ¹³¹I, ¹⁵³Sm, ¹⁷⁷Lu, ¹⁸⁶Re, ²¹³Bi, ²²³Ra, and²²⁵Ac.

The treatment and administration steps described herein may include anysuitable treatment used in accordance with standard practice fortreatment of breast cancer. The treatment is not limited to anyparticular treatment. One skilled in the art will appreciate that anyUnited States Food and Drug Administration (FDA) approved therapeutictreatment or off-label treatment may be used in accordance with themethods provided herein.

In some embodiments, the pharmaceutical composition may also include apharmaceutically acceptable carrier. A pharmaceutically acceptablecarrier may be a pharmaceutically acceptable material, composition, orvehicle that is involved in carrying or transporting a compound ofinterest from one tissue, organ, or portion of the body to anothertissue, organ, or portion of the body. For example, the carrier may be aliquid or solid filler, diluent, excipient, solvent, or encapsulatingmaterial, or some combination thereof. Each component of the carriermust be “pharmaceutically acceptable” in that it must be compatible withthe other ingredients of the formulation. It also must be suitable forcontact with any tissue, organ, or portion of the body that it mayencounter, meaning that it must not carry a risk of toxicity,irritation, allergic response, immunogenicity, or any other complicationthat excessively outweighs its therapeutic benefits.

Expression Ratio of FOXC1/FOXA1 for Use in Breast Cancer Diagnosis

Methods for diagnosing and treating metastatic breast cancer based on anelevated expression ratio of FOXC1/FOXA1 in a population of breastcancer tumor cells from a subject having breast cancer are providedherein. The findings described herein fulfill the unmet need ofdiagnosing clinically occult, asymptomatic metastasis, which allows fortreatment of metastatic cancer earlier than currently possible, when thetumor burden is still small enough that it can be treated effectively.

Distant metastatic spread of cancer cells to other organs from theprimary site of origin currently represents the leading cause ofcancer-related morbidity and mortality (Howlander et al, 1975-2010).According to current oncology practice guidelines, breast cancerpatients (who have already undergone initial treatment for breastcancer) are diagnosed with metastatic spread only when clinicalsuspicion is aroused, either by abnormal values on screening laboratorytests, or appearance of new clinical symptoms, suggestive of dysfunctionin distant organs to which the cancer has metastasized. Even in thosepatients undergoing routine surveillance during follow-up, mostrecurrent breast cancer (unlike primary breast cancer) is alreadysymptomatic at the time of diagnosis (Ghezzi et al., 1994; Tomin andDonegan, 1987). Metastatic cancer cell conglomerates have finallyexceeded the threshold of detection on conventional imaging tests suchas Positron Emission Tomography (PET) (greater than 10 million cells or0.5 cm<3>) or Computer Tomography (CT) scan (greater than 1 billioncells or 1 cm<3>) (Friberg and Mattson, 1997). Once abnormal orsuspicious masses are found in locations suspected to representmetastatic disease, an image-guided biopsy to establish a tissuediagnosis of breast cancer metastasis is obtained. Tissue diagnosis isimportant to establish, as palliative intent chemotherapy cannotethically be undertaken without proof of the existence of metastaticdisease. Starting treatment following a diagnosis of metastatic canceris likely to represent “too little too late” as the tumor burden isoverwhelmingly large. Such an approach is less likely to meet with afavorable outcome and certainly would not be a curative one. On theother hand, early detection of such a metastasis, if possible, at thepre-symptomatic stage, would allow starting potentially life-prolongingtreatments before metastatic tumor burden is allowed to reachoverwhelming proportions.

Currently, it is still not possible to diagnose clinically occult,asymptomatic metastasis early enough, at a point in time when the totalmetastatic tumor burden is small enough that it can still potentially betreated effectively. Before drugs can be rationally designed to targetthe metastatic spread of breast cancer, understanding of the molecularmechanisms that are driving this process must be improved and expanded.Such an understanding is also critical to the development of suitablebiomarkers that would allow early detection of the metastatic phenotype.Despite the fact that several prospective studies have been conducted totry and identify screening biomarkers for breast cancer metastasis, suchas liver function tests, none have been found to have sufficientsensitivity or specificity for this purpose (Crivellari et al., 1995).Studies with circulating tumor markers, while more promising, have notyet led to recommendations supporting their routine use in the ongoingsurveillance of patients already treated for primary breast cancer(Guadagni et al., 2001; Kokko et al. 2002; Molina et al., 2010; Molinaet al., 2003; Nicolini et al., 2006). Several multicenter, randomizedcancer surveillance trials evaluating various follow-up strategies(routine blood tests and imaging tests) have neither been successful indemonstrating earlier diagnosis of metastasis nor have achieved anysurvival advantage as a result of any intensive surveillance measures(Ghezzi et al., 1994; Delturco et al., 1994; Palli et al., 1999).

A traditional clinical measure of advanced disease predictive of anelevated risk of future distant metastasis is the presence of cancercells in the lymph nodes. Lymph node status at the time of diagnosis hasvery important clinical implications. Currently, if a patient diagnosedwith breast cancer is found to have lymph node involvement (lymph node“positive”; LNP), it is the basis of recommending adjuvant chemotherapy.However, it is known from historical observations that not all patientswho are lymph node positive actually go on to manifest with distantmetastatic disease despite the predicted elevated risk, even in theabsence of adjuvant treatment. Since there is not a good way to predictwho will or will not develop distant metastatic disease in the lymphnode positive group, the overtreatment of some to benefit others iscurrently considered acceptable in clinical practice. By the same token,if a patient diagnosed with breast cancer is found not to have lymphnode involvement (lymph node “negative”; LNN), a favorable prognosis ispredicted and is the basis of often withholding adjuvant chemotherapy inan attempt to minimize unnecessary side effects of chemotherapy. Somepatients who are lymph node negative still go on to manifest withdistant metastatic disease despite the predicted low risk. There isclearly a need to improve the prediction of cancer metastasis in bothLNP and LNN groups. This would help to reduce the incidence of bothovertreatment and undertreatment of such patients.

The process of cancer metastasis is not random. Rather, it consists of aseries of linked, sequential steps by which non-migratory cancer cellsof epithelial (E) origin transform into migratory mesenchymal (M) cells(epithelial-to-mesenchymal transition or EMT). They then detach fromneighboring cells, move freely through adjacent tissues, enter thebloodstream leaving their tissue site of origin, manage to survive inthis new migratory environment, and finally exit into a new destinationtissue and colonize it, usually undergoing a reverse change referred toas mesenchymal-to-epithelial transition (MET) (Thiery et al., 2009; Yangand Weinberg, 2008). EMT is a critical precursor event that enablescancer cells to metastasize. Traditionally, E and M cell surface markershave been used to define and follow such cells as they undergo dynamictransition between E and M states. However, recent findings wouldsuggest that cell surface E and M markers may not always be accurate inreflecting the subtle changes along the EMT spectrum. For example, lossof expression of E-cadherin, an epithelial cell surface marker, iswidely believed to be requisite for and synonymous with acquisition of amesenchymal phenotype. Yet, it has been shown that loss of E-cadherin isnot necessary for functional EMT to occur (Hollestelle et al., 2013;Shamir et al., 2014). Thus, methods relying solely on cell surfaceexpression of E and M marker expression may not be accurate in capturingthe true polarization state of a cell, especially cells that are“poised” for such change and are precursors of the metastatic process,but have not yet manifested with overt changes in cell surface markerexpression.

Transcription factor (TF) Forkhead box C1 (FOXC1), strongly associatedwith the basal-like and claudin-low breast cancer molecular subtypes(see Ray et al., 2010), is a powerful epithelial-to-mesenchymaltransition (EMT) inducer and is also a marker of stem/progenitor cells.In contrast, TF Forkhead box A1 (FOXA1), strongly associated withluminal subtypes, is an EMT repressor and a luminal differentiationmarker, thus seemingly exerting reciprocally opposite transcriptionaleffects to that of FOXC1. It was hypothesized that effective EMT programactivation status in breast cancer might be better predicted byexamining the expression ratio of an EMT inducer and EMT repressor, suchas FOXC1/FOXA1, theoretically being more reflective of nettranscriptional effect than either component alone.

As shown in Example 1 below, elevated FOXC1/FOXA1 expression ratioindicates EMT program activation in breast cancer, and predicts theassociated occurrence of lymph-node-independent distant metastasis anddeath in human patients. These findings allow for the early(pre-symptomatic) diagnosis of clinically occult (node negative)metastasis by using the FOXC1/FOXA1 ratio as a biomarker of metastasisand permit institution of appropriate therapy earlier than currentlypossible. The current study provided herein improves the understandingof EMT and highlights the importance of future studies geared towardsunraveling mechanisms involved in regulating FOXC1 and FOXA1 expressionin breast cancer.

Provided herein are methods of diagnosing and/or treating EMT of cancercells. In certain embodiments, a method of treating EMT of cancer cellsmay comprise detecting an expression level of FOXC1 in a population ofbreast cancer tumor cells from the subject; detecting an expressionlevel of FOXA1 in the population of breast cancer tumor cells; andadministering a treatment for EMT of cancer cells if an expression ratioof FOXC1/FOXA1 is elevated as compared to a control. In certainembodiments, the subject may be lymph node negative.

Also provided are methods of diagnosing and/or treating metastaticbreast cancer. In certain embodiments, a method of treating metastaticbreast cancer in a subject may comprise detecting an expression level ofFOXC1 in a population of breast cancer tumor cells from the subject;detecting an expression level of FOXA1 in the population of breastcancer tumor cells; and administering a treatment for metastatic breastcancer to the subject if an expression ratio of FOXC1/FOXA1 in thepopulation of breast cancer tumor cells is elevated as compared to acontrol. In certain embodiments, the subject may be lymph node negative.

Also provided in certain embodiments are methods of predicting theassociated occurrence of lymph-node independent distant metastasis. Incertain embodiments, a method of predicting the associated occurrence oflymph-node independent distant metastasis in a subject may comprisedetecting an expression level of FOXC1 in a population of breast cancertumor cells from the subject; detecting an expression level of FOXA1 inthe population of breast cancer tumor cells; and predicting that thesubject may have an associated occurrence of lymph-node independentdistant metastasis if an expression ratio of FOXC1/FOXA1 is elevated inthe population of breast cancer tumor cells as compared to a control.

As provided herein, methods used to determine the expression level ofFOXC1 and FOXA1 may include any suitable method, including but notlimited to, immunohistochemistry (or other immunoassay), PCR, RT-PCR,qRT-PCR (or any other PCR-based method), and/or the methods, assays andmaterials described in International Application Nos. PCT/US10/44817entitled “Methods for Diagnosis, Prognosis, and Treatment of Primary andMetastatic Basal-Like Breast Cancer and Other Cancer Types;” and PCT/US12/23871 entitled “FOXC1 Antibodies and Methods of Their Use;” thesubject matter of both of which are hereby incorporated by reference asif fully set forth herein.

Detecting the expression level of FOXC1 and/or FOXA1 may be accomplishedby an in vitro immunoassay, such as immunocytochemistry (ICC),immunohistochemistry (IHC), Western blot or fluorescent in situhybridization (FISH). Alternatively, an in vivo imaging modality may beused, such as magnetic resonance imaging (MRI), positron emissiontomography (PET) or microPET, computed tomography (CT), PET/CTcombination imager, cooled charged coupled device (CCD), camera opticalimaging, optical imaging and single photon emission computed tomography(SPECT). When the presence of FOXC1 and/or FOXA1 is determined by an invivo method, the FOXC1 and/or FOXA1 antibody or functional fragmentthereof should be conjugated to an intracellular delivery agent tofacilitate deliver of the antibody or functional fragment thereof to thecytoplasm of target cells.

In certain embodiments, an anti-FOXC1 monoclonal antibody may be used todetect expression level of FOXC1 in a cell. As described in detail inPCT/US12/23871, the antibody may specifically bind a target antigenicpeptide sequence of human FOXC1 (FIG. 37; SEQ ID NO: 1). In one aspect,the target antigenic peptide sequence is 5′-AHAEQYPGGMARAYGPYTPQPQPKD-3′(SEQ ID NO:2), which corresponds to amino acids 51 to 75 of SEQ ID NO: 1(see FIG. 37). A cysteine residue may be added to the N-terminus (i.e.,5′-C-AHAEQYPGGMARAYGPYTPQPQPKD-3′ (SEQ ID NO:3)) to assist inconjugation to the carrier protein as necessary. In certain embodiments,an anti-FOXA1 antibody may be used to detect the expression level ofFOXA1 in a cell. The antibody may specifically bind a target antigenicpeptide sequence of human FOXA1 (FIG. 38; SEQ ID NO:4; NCBI ReferenceSequence: NP_004487.2).

In certain embodiments, the methods described herein may be used totreat metastatic breast cancer. For example, determination of theexpression levels of FOXC1 and FOXA1 can be used to dictate theadministration of a therapeutic agent when the expression ratio of FOXC1to FOXA1 is elevated as compared to a control. The method of treatingmetastatic breast cancer includes a step of administering atherapeutically effective amount or dose of a treatment for metastaticbreast cancer.

An “elevated” expression ratio is typically in comparison to a control.In certain embodiments, the control is a cutoff expression ratio. Insome aspects, a cutoff expression ratio may be established using a setof FOXC1/FOXA1 expression ratios from a population of relevant subjects.For example, the set of FOXC1/FOXA1 expression ratios are from apopulation of node-negative breast cancer patients. Alternatively, theFOXC1/FOXA1 expression ratios are from a population of patients having aspecific type of breast cancer (e.g., basal-like breast cancer patients)or population of patients having a cross-section of all types of breastcancer. In these embodiments, the cutoff expression ratio is determinedby selecting a FOXC1/FOXA1 expression ratio within the population thatfalls at or higher than the 50th percentile line, at or higher than the60th percentile line, at or higher than the 70th percentile line, at orhigher than the 75th percentile line, at or higher than the 80thpercentile line, at or higher than the 85th percentile line, at orhigher than the 90th percentile line, or at or higher than the 95thpercentile line. In one preferred embodiment, the cutoff expressionratio falls at or above the 80th percentile line. Within theseembodiments, an “elevated” FOXC1/FOXA1 expression ratio falls above thecutoff expression ratio.

In other embodiments, the control is an index of expression ratios. Forexample, a set of FOXC1/FOXA1 expression ratios from a population ofrelevant subjects may be used to establish a standard curve or referenceindex of FOXC1/FOXA1 expression ratios by plotting the FOXC1/FOXA1expression ratios against a specific clinical outcome measure such aspresence of metastatic disease, overall survival, breast cancer specificsurvival, recurrence free survival, metastasis free survival, or othersuitable diagnostic or prognostic outcome measures. Such a standardcurve or reference index may be used to categorize or stage a subject'sindividual FOXC1/FOXA1 expression ratio such that if a FOXC1/FOXA1expression ratio falls in an upper range of ratios within a standardcurve or reference index that correlates to an abnormal condition (e.g.,metastatic disease) or outcome (e.g., survival), the subject's ratio isconsidered to be “elevated”.

In certain embodiments, a node-negative breast cancer patient that has aFOXC1/FOXA1 expression ratio that is in the highest 20% of a populationof node-negative breast cancer patients, or is above the 80th percentileof a population of node negative breast cancer patients (i.e., above thecontrol cutoff ratio) indicates or can predict early stages ofmetastasis before other measurable symptoms occur. [0035] Overallsurvival of a subject with breast cancer may also be predicted if thesubject has an elevated FOXC1/FOXA1 expression ratio as compared to acontrol. In certain embodiments, when the expression ratio ofFOXC1/FOXA1 in a population of breast cancer tumor cells from thesubject falls at or higher than the 50th percentile line, at or higherthan the 60th percentile line, at or higher than the 70th percentileline, at or higher than the 75th percentile line, at or higher than the80th percentile line, at or higher than the 85th percentile line, at orhigher than the 90th percentile line, or at or higher than the 95thpercentile line, the subject may have a significantly decreased 10 yearoverall survival as compared to the 10 year survival of a population ofpatients that have all types of breast cancer (see FIG. 35). In certainembodiments, when the expression ratio of FOXC1/FOXA1 in the populationof breast cancer tumor cells from a node-negative breast cancer patientis in the highest 20% of a population of node-negative breast cancerpatients, or is at or above the 80th percentile of a population of nodenegative breast cancer patients (i.e., above or higher than the controlcutoff ratio), the subject may have a significantly decreased 10 yearoverall survival as compared to a population of node-negative breastcancer patients that have a FOXC1/FOXA1 expression ratio that is lowerthan the 80th percentile of a population of node negative breast cancerpatients (see FIG. 36).

“Treating” or “treatment” of a condition may refer to preventing thecondition, slowing the onset or rate of development of the condition,reducing the risk of developing the condition, preventing or delayingthe development of symptoms associated with the condition, reducing orending symptoms associated with the condition, generating a complete orpartial regression of the condition, or some combination thereof.Treatment may also mean a prophylactic or preventative treatment of acondition.

As used herein, a “subject” refers to a mammal, such as a human. In someembodiments, the subject is a patient.

In some embodiments, the treatment used in the methods herein may beadministered as a combination of one or more therapeutic agents for thetreatment of metastatic breast cancer. “A combination” or “incombination with,” as used herein, means in the course of treating thesame cancer in the same subject using two or more agents, drugs,treatment regimens, treatment modalities or a combination thereof, inany order. This includes simultaneous administration, as well as in atemporally spaced order of up to several days apart. Such combinationtreatment may also include more than a single administration of any oneor more of the agents, drugs, treatment regimens or treatmentmodalities. Further, the administration of the two or more agents,drugs, treatment regimens, treatment modalities or a combination thereofmay be by the same or different routes of administration. In someembodiments, the treatment used in the methods herein may beadministered in combination with local therapy, such as surgery orradiation therapy, or a combination thereof.

Examples of therapeutic agents that may be administered as a treatmentinclude, but are not limited to, chemotherapeutic agents, therapeuticantibodies and fragments thereof, toxins, radioisotopes, enzymes (e.g.,enzymes to cleave prodrugs to a cytotoxic agent at the site of thetumor), nucleases, hormones, immunomodulators, antisenseoligonucleotides, nucleic acid molecules (e.g., mRNA molecules, cDNAmolecules or RNAi molecules such as siRNA or shRNA), chelators, boroncompounds, photoactive agents and dyes. The therapeutic agent may alsoinclude a metal, metal alloy, intermetallic or core-shell nanoparticlebound to a chelator that acts as a radiosensitizer to render thetargeted cells more sensitive to radiation therapy as compared tohealthy cells.

Chemotherapeutic agents that may be used in accordance with theembodiments described herein are often cytotoxic or cytostatic in natureand may include, but are not limited to, alkylating agents,antimetabolites, anti-tumor antibiotics, topoisomerase inhibitors,mitotic inhibitors hormone therapy, targeted therapeutics andimmunotherapeutics. In some embodiments the chemotherapeutic agents thatmay be used as therapeutic agents in accordance with the embodiments ofthe disclosure include, but are not limited to, 13-cis-Retinoic Acid,2-Chlorodeoxyadenosine, 5-Azacitidine, 5-Fluorouracil, 6-Mercaptopurine,6-Thioguanine, actinomycin-D, adriamycin, aldesleukin, alemtuzumab,alitretinoin, all-transretinoic acid, alpha interferon, altretamine,amethopterin, amifostine, anagrelide, anastrozole, arabinosylcytosine,arsenic trioxide, amsacrine, aminocamptothecin, aminoglutethimide,asparaginase, azacytidine, bacillus calmette-guerin (BCG), bendamustine,bevacizumab, bexarotene, bicalutamide, bortezomib, bleomycin, busulfan,calcium leucovorin, citrovorum factor, capecitabine, canertinib,carboplatin, carmustine, cetuximab, chlorambucil, cisplatin, cladribine,cortisone, cyclophosphamide, cytarabine, darbepoetin alfa, dasatinib,daunomycin, decitabine, denileukin diftitox, dexamethasone, dexasone,dexrazoxane, dactinomycin, daunorubicin, decarbazine, docetaxel,doxorubicin, doxifluridine, eniluracil, epirubicin, epoetin alfa,erlotinib, everolimus, exemestane, estramustine, etoposide, filgrastim,fluoxymesterone, fulvestrant, flavopiridol, floxuridine, fludarabine,fluorouracil, flutamide, gefitinib, gemcitabine, gemtuzumab ozogamicin,goserelin, granulocyte—colony stimulating factor, granulocytemacrophage-colony stimulating factor, hexamethylmelamine, hydrocortisonehydroxyurea, ibritumomab, interferon alpha, interleukin-2,interleukin-11, isotretinoin, ixabepilone, idarubicin, imatinibmesylate, ifosfamide, irinotecan, lapatinib, lenalidomide, letrozole,leucovorin, leuprolide, liposomal Ara-C, lomustine, mechlorethamine,megestrol, melphalan, mercaptopurine, mesna, methotrexate,methylprednisolone, mitomycin C, mitotane, mitoxantrone, nelarabine,nilutamide, octreotide, oprelvekin, oxaliplatin, paclitaxel,palbociclib, pamidronate, pemetrexed, panitumumab, PEG Interferon,pegaspargase, pegfilgrastim, PEG-L-asparaginase, pentostatin,plicamycin, prednisolone, prednisone, procarbazine, proteasomeinhibitors, raloxifene, rituximab, romiplostim, ralitrexed,sapacitabine, sargramostim, satraplatin, sorafenib, sunitinib,semustine, streptozocin, tamoxifen, tegafur, tegafur-uracil,temsirolimus, temozolamide, teniposide, thalidomide, thioguanine,thiotepa, topotecan, toremifene, tositumomab, trastuzumab, tretinoin,trimitrexate, alrubicin, vincristine, vinblastine, vindestine,vinorelbine, vorinostat, Wnt pathway inhibitors (e.g., iCRT3) orzoledronic acid.

Therapeutic antibodies and functional fragments thereof, that may beused as therapeutic agents in accordance with the embodiments of thedisclosure include, but are not limited to, alemtuzumab, bevacizumab,cetuximab, edrecolomab, gemtuzumab, ibritumomab tiuxetan, panitumumab,rituximab, tositumomab, and trastuzumab and other antibodies associatedwith breast cancer.

Toxins that may be used as therapeutic agents in accordance with theembodiments of the disclosure include, but are not limited to, ricin,abrin, ribonuclease (RNase), DNase I, Staphylococcal enterotoxin-A,pokeweed antiviral protein, gelonin, diphtheria toxin, Pseudomonasexotoxin, and Pseudomonas endotoxin.

Radioisotopes that may be used as therapeutic agents in accordance withthe embodiments of the disclosure include, but are not limited to, ³²P,⁸⁹Sr, ⁹⁹Y, ^(99m)Tc, ⁹⁹Mo, ¹³¹I, ¹⁵³Sm, ¹⁷⁷Lu, ¹⁸⁶Re, ²¹³Bi, ²²³Ra, and²²⁵Ac.

The treatment and administration steps described herein may include anysuitable treatment used in accordance with standard practice fortreatment of breast cancer. The treatment is not limited to anyparticular treatment. One skilled in the art will appreciate that anyUnited States Food and Drug Administration (FDA) approved therapeutictreatment or off-label treatment may be used in accordance with themethods provided herein.

As provided herein, a therapeutically effective amount or effectiveamount is an amount of a treatment that will yield the most effectiveresults in terms of efficacy of treatment in a given subject orpopulation of cells. This amount will vary depending upon a variety offactors, including but not limited to the characteristics of thetreatment, the physiological condition of the subject (including age,sex, disease type and stage, general physical condition, responsivenessto a given dosage, and type of medication) or cells, the nature of thepharmaceutically acceptable carrier or carriers in the formulation, andthe route of administration. Further, an effective or therapeuticallyeffective amount may vary depending on whether the treatment isadministered alone or in combination with another compound, drug,therapy or other therapeutic method or modality. One skilled in theclinical and pharmacological arts will be able to determine an effectiveamount or therapeutically effective amount through routineexperimentation, namely by monitoring a cell's or subject's response toadministration of the treatment and adjusting the dosage accordingly.For additional guidance, see Remington: The Science and Practice ofPharmacy, 21<st>Edition, Univ. of Sciences in Philadelphia (USIP),Lippincott Williams & Wilkins, Philadelphia, Pa., 2005, which is herebyincorporated by reference as if fully set forth herein.

The treatment as described herein may be administered by any suitableroute of administration, alone or as part of a pharmaceuticalcomposition. A route of administration may refer to any administrationpathway known in the art, including but not limited to aerosol, enteral,nasal, ophthalmic, oral, parenteral, rectal, transdermal (e.g., topicalcream or ointment, patch), or vaginal. “Transdermal” administration maybe accomplished using a topical cream or ointment or by means of atransdermal patch. “Parenteral” refers to a route of administration thatis generally associated with injection, including infraorbital,infusion, intraarterial, intracapsular, intracardiac, intradermal,intramuscular, intraperitoneal, intrapulmonary, intraspinal,intrastemal, intrathecal, intrauterine, intravenous, subarachnoid,subcapsular, subcutaneous, transmucosal, or transtracheal.

In some embodiments, the pharmaceutical composition may also include apharmaceutically acceptable carrier. A pharmaceutically acceptablecarrier may be a pharmaceutically acceptable material, composition, orvehicle that is involved in carrying or transporting a compound ofinterest from one tissue, organ, or portion of the body to anothertissue, organ, or portion of the body. For example, the carrier may be aliquid or solid filler, diluent, excipient, solvent, or encapsulatingmaterial, or some combination thereof. Each component of the carriermust be “pharmaceutically acceptable” in that it must be compatible withthe other ingredients of the formulation. It also must be suitable forcontact with any tissue, organ, or portion of the body that it mayencounter, meaning that it must not carry a risk of toxicity,irritation, allergic response, immunogenicity, or any other complicationthat excessively outweighs its therapeutic benefits.

The following examples are intended to illustrate various embodiments ofthe invention. As such, the specific embodiments discussed are not to beconstrued as limitations on the scope of the invention. It will beapparent to one skilled in the art that various equivalents, changes,and modifications may be made without departing from the scope ofinvention, and it is understood that such equivalent embodiments are tobe included herein. Further, all references cited in the disclosure arehereby incorporated by reference in their entirety, as if fully setforth herein.

Having described the invention with reference to the embodiments andillustrative examples, those in the art may appreciate modifications tothe invention as described and illustrated that do not depart from thespirit and scope of the invention as disclosed in the specification. Theexamples are set forth to aid in understanding the invention but are notintended to, and should not be construed to, limit its scope in any way.The examples do not include detailed descriptions of conventionalmethods. Such methods are well known to those of ordinary skill in theart and are described in numerous publications. All references citedabove and below in the specification are incorporated by reference intheir entirety, as if fully set forth herein.

Example 1: FOXC1 is a Prognostic Biomarker with Functional Significancein Basal-Like Breast Cancer

Gene expression signatures for a basal-like breast cancer (BLBC) subtypehave been associated with poor clinical outcomes. As described below,overexpression of the transcription factor FOXC1 is shown to be aconsistent feature of BLBC compared with other molecular subtypes ofbreast cancer. Elevated FOXC1 expression predicted poor overall survivalin BLBC (P=0.0001), independently of other clinicopathologic prognosticfactors including lymph node status, along with a higher incidence ofbrain metastasis (P=0.02) and a shorter brain metastasis—free survivalin lymph node—negative patients (P<0.0001). Ectopic overexpression ofFOXC1 in breast cancer cells increased cell proliferation, migration,and invasion, whereas shRNA-mediated FOXC1 knockdown yielded oppositeeffects. These findings identify FOXC1 as a theranostic biomarker thatis specific for BLBC, offering not only a potential prognostic candidatebut also a potential molecular therapeutic target in this breast cancersubtype.

Materials and Methods

Microarray Analysis.

Publicly available datasets of human breast cancer gene expressionmicroarrays (Richardson et al. 2006; Farmer et al. 2005; Hess et al.2006; Ivshina et al. 2006; Miller et al. 2005; van de Vijver et al.2002; Herschkowitz et al. 2007; Sorlie et al. 2003; Wang et al. 2005;Pawitan et al. 2005) comprising of raw expression level data files andthe ExpO Project database of the International Genomics Consortium (IGC)at https://expo.intgen.org were downloaded were analyzed usingGenespring GX 10.0 software (Agilent Technologies) (see Table 1 below).A total of 2,073 breast cancer patient samples were analyzed. For cDNAarrays (3 of 11 data sets), the loge normalized signal intensity valueswere directly imported into the Genespring software platform, obtainedfrom the respective public web repository. For microarray raw dataobtained from Affymetrix arrays (8 of 11 data sets), signal intensitieswere obtained using the Robust Multi-chip Averaging (RMA) algorithm toperform background correction, normalization and summarization ofprobe-level raw data. All values underwent baseline transformation tomedian of all samples in a particular dataset on a (per gene)/(perprobe) set basis.

TABLE 1 Summary of analyzed microarray datasets. Reference SampleComplete IHC Survival No. Array Name Platform Technology Size DataAnalysis ExpO Affymetrix U133 plus 2.0 250 − − 9 Richardson et al.Affymetrix U133 plus 2.0 47 − − 10 Farmer et al. Affymetrix U133A 49 − −11 Hess et al. Affymetrix U133A 133 + − 12 Ivshina et al. AffymetrixU133A 249 − − 13 Miller et al. Affymetrix U133A 251 − − 14 van de Vijveret al. cDNA 295 − + 15 Herschkowitz et al. cDNA 232 − + 16 Sorlie et al.cDNA 122 − + 17 Wang et al. Affymetrix U133A 286 − + 18 Pawitan et al.Affymetrix U133A 159 − +

All microarray datasets used in this study are from publicly availabledatabases, and such databases require that the gene expression raw data,deposited by the original investigators, meet stringent quality controlcriteria prior to acceptance. Furthermore, each dataset has been earlierreported in the literature and individual quality control measures arereported in the original references. As such, in the present study,quality control measures were taken to confirm prior established dataquality, rather than as an initial step to document data quality. Arrayquality control was performed using 3D Principal Component Analysis(PCA) plots, Internal Controls comprising of 3′/5′ ratios for a set ofspecific housekeeping gene probe sets, and Hybridization Controls. A3′/5′ ratio of greater than 3 was considered to be unacceptable(representative of either degraded starting RNA or problem with the cDNAsynthesis reaction). The signal intensities of pre-mixed hybridizationcontrol transcripts added to the hybridization mix in known staggeredconcentrations should increase as expected with the known staggeredconcentrations. Deviation from the expected intensity profile of thesecontrols, as assessed by visual inspection of Hybridization Controlplots, was considered to be unacceptable (representative of a problemeither with the hybridization or washing process). Based on thesecriteria, only one array (from the Richardson et al. dataset) among atotal of 2,073 examined arrays was removed. The PCA scores of each arraywere plotted in 3D in order to examine the clustering pattern ofsamples. Three major clusters were observed in each dataset, consistentwith the expected biologic variation in this population resulting insegregation into the three molecular subtypes—luminal, HER2 andbasal-like. Probes from the spotted arrays were filtered based on flagvalues. Otherwise they were filtered based on signal intensity values sothat values between 20.0 and 100.0 percentiles in a given dataset wereretained.

For identification of the molecular subtypes, we employed the commonlyused 306-member Intrinsic Gene Set (IGS) (Hu et al. 2006). Only 293genes of the original 306-gene panel were represented on the microarrayplatform of our test dataset that was selected based on its inclusion ofnormal breast tissue samples (Richardson et al 2006). We subjected alldatasets to a hierarchical clustering algorithm employing a Pearsonuncentered similarity metric and the average linkage rule based on the293-gene IGS. Datasets were then clustered into luminal A/B, HER2, andbasal-like subtypes based on IGS. In the Richardson et al. dataset, 12samples were excluded as they were derived from normal organoidpreparations and not normal breast tissue, 4 BRCA positive samples wereexcluded to reduce bias, 1 sample was excluded for not meeting qualitycontrol standards and 1 sample classified by the authors as basal-likeclustered with the luminal subtype and was thus excluded from theanalysis.

To determine the correlation between FOXC1 and triple-negative status,we searched for publicly available datasets that contained complete ER,PR, and HER2 expression profiles of each breast cancer specimen based onimmunohistochemical analysis. Only one such dataset (Hess et al.) wasidentified (Hess et al. 2006).

Average relative mRNA levels (mean log₂ signal intensity) for each IGSgene and for reported markers of BLBC in the literature (αB-crystallin(Moyano et al. 2006), moesin (Charafe-Jauffret et al. 2007), CD109(Hasegawa et al. 2008), p-Cadherin, EGFR (Nielsen et al. 2004), CK5(Nielsen et al. 2004; Korsching et al. 2008), CK14 (Korsching et al.2008), CK17 (Korsching et al. 2008), c-Kit (Nielsen et al. 2004), ITGB4(Lu et al. 2008), and FOXC2 (Mani et al. 2007)) were determinedaccording to molecular subtype. Expression values for some genes werenot normally distributed for which reason we employed nonparametricanalysis (Mann-Whitney Test) in comparing Basal-like group vs. poolednon-Basal-like group expression values (loge normalized signalintensity) for each gene. All statistical analyses were performed usingSAS software (Version 9.1.3, SAS Institute, Cary, N.C.). A stepwiselogistic regression analysis was performed to identify the gene mostcharacteristic of the basal-like group. In view of the small sample sizeof the Richardson et al. dataset (with highly predictive covariatesresulting in non-convergence), Firth's modified logistic regressionanalysis used to reduce the bias of maximum likelihood estimation inthis array. Statistical significance for each of these analyses wasdefined as P<0.05. To maintain statistical power, each dataset wasanalyzed independently as shown below in Tables 2-5.

TABLE 2 Statistical analysis of biomarker in molecular subgroupsclassified by IGS in the Richardson et al. breast cancer microarraydataset (2). Univariate Wilcoxon Rank Sum Test Multivariate NormalLuminal HER2 Basal-like (Basal-like Logistic Mean ± SD Mean ± SD Mean ±SD Mean ± SD vs. Other) Regression Gene (Median) (Median) (Median)(Median) P-value P-value† FOXC1 −0.11 ± 0.73 (−0.23) −1.63 ± 0.38(−1.60) −0.99 ± 0.71 (−1.03) 3.61 ± 0.75 (3.63) <0.0001 0.0006 CRYAB−1.87 ± 0.39 (1.87) −1.61 ± 1.11 (−1.66) −1.82 ± 0.78 (−1.93) 1.34 ±1.30 (1.27) 0.001 NS KRT5 2.98 ± 0.38 (2.72) −1.17 ± 0.98 (−1.32) −1.35± 1.02 (−1.31) 0.81 ± 1.48 (1.14) NS NS KIT 3.08 ± 0.47 (3.07) −0.93 ±0.88 (−1.03) −1.21 ± 0.76 (−1.16) 0.60 ± 1.22 (0.32) NS NS CDH3 1.21 ±0.43 (1.35) −1.13 ± 0.66 (−1.40) −0.11 ± 0.92 (−0.20) 0.64 ± 1.24 (0.97)0.036 NS MSN 0.18 ± 0.33 (0.15) −0.32 ± 0.61 (−0.41) −0.57 ± 0.44(−0.51) 0.60 ± 0.88 (0.70) 0.002 NS KRT17 2.84 ± 0.25 (2.81) −1.03 ±0.80 (−1.05) −0.79 ± 1.13 (−0.95) 0.92 ± 1.78 (1.05) NS NS EGFR 0.32 ±0.40 (0.39) −0.50 ± 0.30 (−0.51) 0.02 ± 0.67 (0.19) 0.25 ± 0.55 (0.27)0.044 NS KRT14 3.44 ± 0.51 (3.60) −1.30 ± 1.84 (−0.82) −2.15 ± 1.82(−1.49) 0.69 ± 2.53 (0.59) NS NS CD109 −0.46 ± 0.62 (−0.25) −0.45 ± 0.91(−0.19) −0.77 ± 1.02 (−1.08) 0.46 ± 0.98 (0.63) 0.004 NS ITGB4 0.40 ±0.39 (0.34) −0.32 ± 0.35 (−0.37) 0.24 ± 0.74 (0.01) 0.19 ± 0.87 (0.13)NS NS FOXC2 0.09 ± 0.13 (0.07) −0.01 ± 0.20 (−0.03) −0.01 ± 0.16 (0.03)0.04 ± 0.26 (0.01) NS NS Values in each molecular subtype column are themean ± SD of the log2 normalized signal intensity for the bestrepresentative cDNA probe for that gene. NS, P > 0.05. †Firth's modifiedlogistic regression analysis used to reduce the bias of maximumlikelihood estimation in this array (characterized by small sample sizewith highly predictive covariates resulting in non-convergence).Basal-like (yes = 1, no = 0) was used as a dependent variable.

TABLE 3 Statistical analysis of biomarker in molecular subgroupsclassified by IGS in the Ivshina et al. breast cancer microarray dataset(14). Univariate Wilcoxon Multivariate Luminal HER2 Basal-like Rank SumTest Logistic Mean ± SD Mean ± SD Mean ± SD (Basal-like vs. Other)Regression Gene (Median) (Median) (Median) P-value P-value FOXC1  0.02 ±0.27 (−0.01)  0.00 ± 0.28 (−0.05) 1.88 ± 0.71 (1.92) <0.0001 0.0033 CDH3−0.06 ± 0.76 (−0.18) 0.90 ± 0.84 (0.73) 1.94 ± 0.92 (2.07) <0.0001010199 CRYAB −0.03 ± 0.79 (−0.06) −0.30 ± 0.44 (−0.40) 1.94 ± 1.23(2.20) <0.0001 NS EGFR −0.05 ± 0.94 (−0.17) 0.41 ± 0.86 (0.28) 1.20 ±0.57 (1.15) <0.0001 NS KRT17  0.13 ± 0.99 (−0.11) 0.51 ± 1.10 (0.09)2.06 ± 1.54 (1.99) <0.0001 NS KRT5  0.09 ± 1.08 (−0.21) 0.12 ± 0.85(0.00) 2.20 ± 1.34 (2.20) <0.0001 NS MSN   −0.10 ± 0.48 (−0.04) -  0.06± 0.35 (−0.12) 0.74 ± 0.42 (0.82) <0.0001 NS ITGB4 −0.03 ± 0.44 (−0.07)0.28 ± 0.46 (0.35) 0.45 ± 0.65 (0.26) 0.0016 NS KIT 0.11 ± 1.00 (0.00)−0.27 ± 0.87 (−0.46) 1.07 ± 1.45 (1.25) 0.0011 NS KRT14 −0.24 ± 1.96(−0.15) −0.30 ± 1.47 (−0.57) 1.99 ± 2.08 (1.55) 0.0001 NS FOXC2 0.00 ±0.17 (0.00) 0.05 ± 0.16 (0.05) 0.07 ± 0.26 (0.02) NS NS Values in eachmolecular subtype column are the mean ± SD of the log2 normalized signalintensity for the best representative cDNA probe for that gene. NS, P >0.05. * CD109 does not have any representative probes on this microarrayplatform. In the multivariate logistic regression analysis, dependentvariable is basal-like.

TABLE 4 Statistical analysis of biomarker in molecular subgroupsclassified by IGS in the Miller et al. breast cancer microarray dataset(15). Univariate Wilcoxon Multivariate Luminal HER2 Basal-like Rank SumTest Logistic Mean ± SD Mean ± SD Mean ± SD (Basal-like vs. Other)Regression Gene (Median) (Median) (Median) P-value P-value FOXC1  0.02 ±0.28 (−0.01) −0.04 ± 0.29 (−0.12) 1.86 ± 0.71 (1.90) <0.0001 0.0003 CDH3−0.05 ± 0.76 (−0.16) 0.87 ± 0.94 (0.75) 1.95 ± 0.91 (2.09) <0.00010.0153 KRT17  0.13 ± 1.01 (−0.09) 0.48 ± 1.11 (0.08) 2.05 ± 1.54 (1.99)<0.0001 NS EGFR −0.04 ± 0.94 (−0.17) 0.39 ± 0.88 (0.08) 1.21 ± 0.57(1.16) <0.0001 NS MSN −0.09 ± 0.47 (−0.03) −0.13 ± 0.42 (−0.19) 0.74 ±0.42 (0.82) <0.0001 NS CRYAB −0.01 ± 0.78 (−0.05) −0.38 ± 0.48 (−0.41)1.94 ± 1.24 (2.20) <0.0001 NS KRT5  0.10 ± 1.08 (−0.16) 0.07 ± 0.88(0.00) 2.19 ± 1.33 (2.18) <0.0001 NS KRT14 −0.26 ± 1.96 (−0.14) −0.40 ±1.50 (−0.61) 1.94 ± 2.07 (1.50) <0.0001 NS ITGB4 −0.02 ± 0.45 (−0.07)0.21 ± 0.46 (0.27) 0.45 ± 0.65 (0.26) 0.002 NS KIT 0.12 ± 1.01 (0.01)−0.27 ± 0.86 (−0.43) 1.07 ± 1.45 (1.25) 0.001 NS FOXC2 0.00 ± 0.17(0.00) 0.03 ± 0.18 (0.05) 0.06 ± 0.26 (0.02) NS NS Values in eachmolecular subtype column are the mean ± SD of the log2 normalized signalintensity for the best representative cDNA probe for that gene. NS, P >0.05. * CD109 does not have any representative probes on this microarrayplatform. In the multivariate logistic regression analysis, dependentvariable is basal-like.

TABLE 5 Statistical analysis of biomarker in molecular subgroupsclassified by IGS in the van de Vijver et al. breast cancer microarraydataset (16). Univariate Wilcoxon Multivariate Luminal HER2 Basal-likeRank Sum Test Logistic Mean ± SD Mean ± SD Mean ± SD (Basal-like vs.Other) Regression Gene (Median) (Median) (Median) P-value P-value FOXC1−0.51 ± 0.21 (−0.50) −0.41 ± 0.21 (−0.41) 0.49 ± 0.42 (0.58) <.00010.0028 CRYAB −0.36 ± 0.24 (−0.36) −0.29 ± 0.19 (−0.29) 0.27 ± 0.47(0.28) <.0001 NS KRT5 −0.56 ± 0.40 (−0.50) −0.45 ± 0.42 (−0.28) 0.16 ±0.56 (0.10) <.0001 0.0084 KIT −0.17 ± 0.24 (−0.16) −0.22 ± 0.25 (−0.19)0.05 ± 0.34 (0.05) <.0001 NS CDH3 −0.49 ± 0.29 (−0.49) −0.14 ± 0.38(−0.15) 0.32 ± 0.31 (0.37) <.0001 NS MSN −0.14 ± 0.17 (−0.13) −0.05 ±0.16 (−0.06) 0.21 ± 0.14 (0.24) <.0001 NS KRT17 −0.33 ± 0.28 (−0.35)−0.22 ± 0.39 (−0.14) 0.21 ± 0.46 (0.17) <.0001 NS EGFR −0.05 ± 0.14(−0.05) −0.01 ± 0.15 (−0.03) 0.07 ± 0.21 (0.06) <.0001 NS KRT14 −0.10 ±0.12 (−0.11) −0.08 ± 0.13 (−0.11) 0.08 ± 0.30 (0.02) 0.0001 NS ITGB4−0.03 ± 0.12 (−0.03) 0.10 ± 0.14 (0.08) 0.12 ± 0.19 (0.12) <.0001 NSValues in each molecular subtype column are the mean ± SD of the log2normalized signal intensity for the best representative cDNA probe forthat gene. NS, P > 0.05. * FOXC2 and CD109 do not have anyrepresentative probes on this microarray platform. In the multivariatelogistic regression analysis, dependent variable is basal-like.

For simplicity of data interpretation, normal breast-like group was notincluded in the analysis. The normal breast-like group resembles normalbreast tissue samples with relatively high expression of genescharacteristic of adipose cells and other non-epithelial cell types andlow expression of luminal epithelial cell genes. Because the normal-likeclassification was developed by training on normal breast tissue, it hasbeen speculated that the normal-like subgroup may be mainly an artifactof having a high percentage of normal “contamination” in tumor specimens(Parker et al. 2009). Other explanations include a group of slow-growingbasal-like tumors that lack the expression of proliferation genes or apotential new subtype called claudin-low tumors (Herschkowitz et al.2007). In addition, only some of the datasets used in our analysiscontain normal-like samples. FOXC1 was not found to be overexpressed inthese samples (data not shown).

Gene Signature Analysis. With the intent of developing a gene signatureassociated with FOXC1 gene expression capable of accurately detectingthe basal-like subtype independent of IGS, the test dataset thatincluded normal breast tissue samples was first analyzed (2). Genes thatshared coordinate upregulation and genes that shared coordinatedownregulation with FOXC1 upregulation were both included. Supervisedstringent inclusion criteria were used based on degree of Pearsoncorrelation coefficients (1.0>r>0.5 for genes with coordinateupregulation and −1.0<r<−0.5 for genes with coordinate downregulation,respectively). Only those genes that maintained their high degree ofcorrelation with FOXC1, independent of their individual correlationswith breast cancer subtypes, were included in the final panel andvalidated in a total of 5 individually analysed microarray datasets(Richardson et al. 2006; Farmer et al. 2005; Ivshina et al. 2006; Milleret al. 2005-2, 13-15) and the ExpO Project Database of the InternationalGenomics Consortium (IGC) at https://expo.intgen.orq). The 30 genes thatmet the inclusion criteria while still allowing for maximalapplicability across earlier generation microarray platforms (i.e.ranking in the top 30 genes associated with FOXC1 expression in 3 ormore of the 5 datasets) are collectively referred to as the FOXC1 genesignature (Table 6).

TABLE 6 Pearson correlation coefficients of the 30 genes associated withFOXC1 gene expression in five microarray datasets (2, 13-15). DatasetGene Richardson Farmer Ivshina Miller No. Symbol et al. ExpO et al. etal. et al. Frequency* 1 FOXC1 1.00 1.00 1.00 1.00 1.00 5 2 OGFRL1 0.860.50 0.49 0.65 0.66 4 3 ROPN1B 0.83 0.75 0.80 0.73 0.73 5 4 ART3 0.830.65 0.59 0.63 0.64 5 5 FABP7 0.82 0.39 0.40 0.57 0.60 3 6 C10orf38 0.820.65 0.70 0.72 0.72 5 7 EN1 0.81 0.74 0.80 0.74 0.73 5 8 KCNK5 0.80 0.630.64 0.65 0.64 5 9 CHODL 0.80 0.60 0.54 0.56 0.57 5 10 PRKX 0.80 0.550.73 0.66 0.66 5 11 C21orf91 0.79 0.56 0.39 0.52 0.53 4 12 GABRP 0.780.70 0.77 0.74 0.74 5 13 ELF5 0.77 0.65 0.63 0.61 0.61 5 14 PAPSS1 0.770.48 0.47 0.54 0.54 3 15 ACTR3B 0.77 0.64 0.63 0.55 0.55 5 16 LMO4 0.760.41 0.59 0.65 0.64 4 17 ZIC1 0.75 0.53 0.61 0.39 0.39 3 18 UGT8 0.750.64 0.46 0.60 0.60 4 19 MICALL1 0.75 0.70 0.78 0.64 0.64 5 20 FOXA1−0.87 −0.75 −0.81 −0.82 −0.82 5 21 MLPH −0.86 −0.70 −0.78 −0.69 −0.69 522 SIDT1 −0.84 −0.58 −0.73 −0.56 −0.55 5 23 AGR2 −0.83 −0.67 −0.71 −0.59−0.59 5 24 SPDEF −0.81 −0.64 −0.72 −0.79 −0.78 5 25 TFF3 −0.80 −0.53−0.67 −0.46 −0.45 3 26 AR −0.80 −0.50 −0.56 −0.58 −0.59 5 27 TBC1D9−0.79 −0.62 −0.66 −0.66 −0.66 5 28 CA12 −0.78 −0.60 −0.66 −0.66 −0.66 529 GATA3 −0.77 −0.56 −0.71 −0.70 −0.70 5 30 GALNT6 −0.75 −0.53 −0.66−0.52 −0.51 5 *Frequency denotes the number of datasets in which thecorrelation of individual genes with FOXC1 expression is present (>0.50for coordinately upregulated genes, and <−0.50 for coordinatelydownregulated genes, respectively).

To validate the ability of this gene signature to identify basal-likebreast cancer, in addition to the aforementioned 5 datasets used torefine the gene signature, another 6 publicly available human breastcancer Affymetrix and cDNA microarray datasets were individually tested(Hess et al. 2006; Herschkowitz et al. 2007; van de Vijver et al. 2002;Sorlie et al. 2003; Wang et al. 2005; Pawitan et al. 2005) representinganalysis in a total of 2,073 breast cancer patients. All datasets weresubjected to a hierarchical clustering algorithm employing a Pearsonuncentered similarity metric and the average linkage rule based on the30-member FOXC1 gene signature. Extent of correct classification ofbreast cancer samples as belonging to the basal-like subtype wascompared to those classified based on IGS.

Survival Analysis.

Next, the potential prognostic importance of FOXC1 mRNA expression inbreast cancer was determined, with particular reference to assessing itsability to correctly predict the survival of patients with basal-likebreast cancer. This analysis was performed with the intent to determinewhether FOXC1 mRNA expression could be used as a stand alone, individualprognostic biomarker for basal-like breast cancer instead of pathologic,immunohistochemical and/or molecular classifiers such as IGS. A295-sample breast cancer oligonucleotide microarray dataset (van deVijver et al. 2002) with follow-up data extending over a 20 year periodwas subjected to analysis. The prognostic significance of FOXC1 was alsoexamined in three additional human breast cancer cDNA datasets: A232-sample dataset (Herschkowitz et al. 2007), a 122-sample dataset(Sorlie et. al. 2003), and a 159-sample dataset (Pawitan et al. 2005).Survival distributions were estimated using Kaplan-Meier methods andcompared using the log-rank test. In multivariate survival analyses, Coxproportional hazard regression model was used incorporating phenotypestatus (basal-like versus non-basal-like), FOXC1 level, age, tumor size,tumor grade, and lymph node status as possible predictors of survival.Proportional hazard assumption was validated using residual plots andproportionality tests. The relative prognostic significance of twoseparate prognostic models was evaluated by comparing the model fitafter adjusting for clinicopathologic variables. One model was based ondichotomous expression of FOXC1 mRNA levels. The other model was basedon the IGS-derived basal-like cluster following hierarchical clustering.The relative prognostic significance of each model was measured usingAkaike's Information Criterion (AIC) to assess the fit of the tworegression models (Akaike 1974).

Association with metastasis to the brain or bone was examined in lymphnode-negative breast cancer patients in the Wang et al. data set (Wanget al. 2005). The Wilcoxon rank sum test was used to assess statisticalsignificance for this comparison. Brain specific and bone-specificmetastasis-free survival was also examined in the same data set.Univariate and multivariate analyses were done using log-rank test andCox regression model, respectively. Variables included in themultivariate analysis were selected based on statistical significance ininitial univariate analysis and included age, tumor size, and lymph nodestatus. Survival plots were created using Kaplan-Meier methods.

Immunohistochemistry and Immunoblotting

Immunohistochemistry was performed using a peroxidase detection systemwith human breast cancer tissue microarrays BRC961 and BR962 (US Biomax)and a polyclonal FOXC1 antibody that does not recognize FOXC2 (LifespanBiosciences). Antibody concentration (1:100) was determined by serialtitration and optimisation of the antibody on test arrays. Briefly,after antigen retrieval, primary antibodies were added, followed by abiotinylated secondary antibody incubation, which then binds toperoxidase-conjugated streptavidin. The signal was developed withdiaaminobenzidine as the chromogen with hematoxylin as counterstain. Theimmunostained slides were evaluated microscopically by estimating theproportion and average intensity of positive tumor cells with nuclearand/or cytoplasmic staining. Immunohistochemical analysis was alsoperformed on 42 triple-negative human breast cancer specimens obtainedfrom the Saint John's Health Center Department of Pathology and JohnWayne Cancer Institute tissue bank in accordance with InstitutionalReview Board approval. Immunoblotting was performed using an antibodyfrom Santa Cruz Biotechnology. Whole cell lysates for western blottingwere generated by cell lysis buffer (50 mM Tris-HCl, pH 7.4, 150 mMNaCl, 2 mM EDTA, 1% NP-40, 10% glycerol) supplemented with a proteaseinhibitor cocktail (Sigma, St Louis, Mo.). Equal amounts of protein wereseparated by 10% SDS-PAGE and then transferred onto a nitrocellulosemembrane. The remaining steps were conducted according to a standardimmunoblotting protocol.

Results and Discussion

Gene expression analysis of publicly available human breast cancermicroarray data sets revealed that the Forkhead-box transcription factorFOXC1, essential for mesoderm tissue development, had significantlyhigher expression in the basal-like subgroup than in other subtypes(FIGS. 1A, 1B, 2 and 3A-C). High FOXC1 expression correlated positivelyand significantly with the basal-like subgroup, as shown in Tables 2-5above. Elevated FOXC1 mRNA expression was also associated withtriple-negative breast cancer, consistent with the notion that 60% to90% of triple-negative breast cancers are basal-like (FIGS. 1C and 3D).A 30-gene FOXC1 signature was derived from correlation with FOXC1expression in six data sets (Table 6, above) and validated in fiveseparate data sets. These genes displayed an overall expression profilethat coincided with the basal-like subgroup clustered by IGS (FIGS. 1Dand 4). Conversely, hierarchical clustering using the FOXC1 genesignature identified the same basal-like subgroup determined by IGS(FIG. 5). Whereas pathway analysis of this gene signature did not yielda dominant pathway (data not shown), some members such as FABP7, GABRP,EN1, KCNK5, ZIC1, ACTR3B, and FOXC1 are notably involved in braindevelopment and brain tumorigenesis, which explains why BLBCpreferentially metastasizes to the brain.

FOXC1 protein expression was then evaluated using immunohistochemistryon breast cancer tissue microarrays (TMA). Strong nuclear FOXC1 stainingwas found in triple-negative TMA samples expressing basal cytokeratins(CK5/6+ and/or CK14+; FIG. 6A) but not in non-triple-negative tumors(data not shown). Cytoplasmic staining of FOXC1 was rare, and it wasnormally concomitant with nuclear staining of FOXC1. This pattern ofsubcellular localization was confirmed in an independent cohort of 42archived triple-negative breast cancer specimens. Positive expression ofFOXC1 (FOXC1+) was associated significantly with expression of basalcytokeratins (FIG. 6B) and displayed a sensitivity of 0.81 and aspecificity of 0.80 in detecting the basal-like phenotype identified bypositive staining of CK5/6 and/or CK14. Absence of CK staining in someFOXC1+/ER−/PR−/HER2− samples in this cohort may reflect inconsistentexpression of these cytokeratins in BLBCs defined by expression arrays(Nielsen et al. 2004). The finding that nuclear FOXC1 was consistentlydetected by immunohistochemistry despite its short protein half-life(<30 minutes; Berry et al. 2006) suggest a robust constitutiveexpression of FOXC1 in BLBC. Analysis of a microarray data set for ahuman breast cancer cell line panel revealed higher FOXC1 expression inBLBC cell lines (FIG. 7), which was confirmed by immunoblotting (FIG.6C).

The prognostic significance of FOXC1 in breast cancer was next examinedin the 295-sample van de Vijver et al. data set (van de Vijver et al.2002). In univariate analysis, overall survival was significantly worsein tumors identified using the 30-gene FOXC1 signature (P=0.0004) orusing elevated FOXC1 mRNA levels alone (P=0.0001; FIG. 8A). Overallsurvival decreased by 35% for each unit increase of relative FOXC1 mRNAlevels. In multivariate analysis, FOXC1 was an independent prognosticindicator of overall survival after adjusting for clinicopathologicvariables such as age, tumor size, and lymph node status (hazard ratio,1.25; 95% confidence interval, 1.02-1.52; P=0.02). Akaike informationcriteria (AIC; Akaike 1974) were used in comparing the fit of the twoseparate prognostic models after adjusting for clinicopathologicvariables. The model based on FOXC1 mRNA expression (AIC, 820.0) wassimilar to the model based on the IGS-derived basal-like cluster (AIC,815) in terms of the model fit predicting survival. The association ofFOXC1 with overall survival was also shown in the 232-sampleHerschkowitz et al. (Herschkowitz et al. 2007), 122-sample Sorlie et al.(Sorlie et al. 2003), and 159-sample Pawitan et al. (Pawitan et al.2005) data sets (FIG. 9). Furthermore, the FOXC1 gene signature and mRNAlevels, like the basal-like phenotype, allowed prognostic stratificationof lymph node-negative breast cancers (P=0.0003) in the van de Vijver etal. data set (an de Vijver et al. 2002; FIG. 8B). In addition, elevatedFOXC1 expression, which was positively associated with brain metastasis(P=0.02) and inversely associated with bone metastasis (P=0.0002) in the286-sample Wang et al. data set (Wang et al. 2005), significantlycorrelated with shorter brain metastasis-free survival (P<0.0001; FIGS.8C and D).

Example 2: Quantitative Measurement of FOXC1 Expression Using RT-PCR canbe Used to Accurately Diagnose Basal-Like Breast Cancer

Gene expression analysis has classified breast cancer into fivemolecular subtypes. Basal-like breast cancer comprises up to 15%-25% ofall breast cancers and is associated with the worst overall survival. Asdescribed in the example above, FOXC1 is a theranostic biomarkerspecific for basal-like breast cancer. Semi-quantitative measurement ofFOXC1 expression (microarray and immunohistochemistry) has been shown asa reliable method to diagnose basal-like breast cancer. These findingsmay be extended and further refined by assessing FOXC1 expression usingqRT-PCR to provide a more quantitatively accurate assay for diagnosingbasal-like breast cancer.

Quantitative RT-PCR gene expression data from 279 formalin-fixedparaffin embedded (FFPE) breast tumors were obtained from a publiclyavailable database (J Clin Oncol. 2009 Mar. 10; 27(8):1160). Thereceiver operating curve-area under the curve (ROC-AUC) was determinedfor FOXC1. A cut-off level was determined to optimize sensitivity andspecificity.

The ROC-AUC for FOXC1 expression (FIG. 10) in predicting basal-likebreast cancer was 0.807. A 74% sensitivity and 78% specificity foridentifying basal-like breast cancer was shown when using the 0.437(49^(th) percentile) cut-off level for FOXC1 expression using qRT-PCR.

Quantitative RT-PCR assessment of FOXC1 is thus proven to be a reliableassay to accurately diagnose basal-like breast cancer. QuantitativeRT-PCR assessment of FOXC1 from FFPE breast tumors is proposed to be auseful adjunct to semi-quantitative assays (microarray andimmunohistochemistry) for the diagnosis of basal-like breast cancer inroutine clinical practice.

Example 3: Prognostic Significance of FOXC1 in Breast Cancer MolecularSubtype Models Utilizing Immunohistochemical Biomarkers

In the studies described herein, the Forkhead-box transcription factorFOXC1, essential for mesoderm tissue development, was been shown to beconsistently overexpressed at both the mRNA and protein levels in BLBC.Elevated FOXC1 mRNA expression was associated with poor overallsurvival, independent of other clinicopathologic prognostic variables,including lymph node status. True to a predilection for brain metastasisdisplayed by patients with BLBC, high FOXC1 mRNA levels were also foundto correlate with the incidence of brain metastasis and withsignificantly shortened brain-metastasis free survival in lymph nodenegative patients. Furthermore, engineered, ectopic overexpression ofFOXC1 in breast cancer cells induced aggressive phenotypic changes suchas increased cellular proliferation, migration and invasion. Knockdownof FOXC1 using shRNA in breast cancer cells with high endogenous levelsof FOXC1 demonstrated loss of aggressive phenotypic features. Theseresults suggest that FOXC1 is a specific prognostic biomarker for BLBCand plays an important role in regulating aggressive cellular traitsassociated with this molecular subtype. It may also serve as a suitabletarget for personalized therapy of patients diagnosed with BLBC. Thesefindings utilizing gene expression profiling strongly support theprognostic significance of FOXC1 mRNA expression in breast cancer.According to the study described below, this finding is translated orcorroborated using assays of protein expression, such asimmunohistochemistry (IHC). Such an assay would be practical andrelevant for implementation into routine clinical practice.

Currently, breast cancer receptor status (ER, PR and HER2) is widelyused to perform prognostic stratification. Recent reports have suggestedusing additional surrogate IHC markers of BLBC in combination to improveprognostic stratification (Rakha et al. 2009; Nielsen et al. 2004;Cheang et al. 2008; Elsheikh et al. 2008). Therefore, threebiomarker-based models of prognostic stratification in breast cancerwere compared: 1) the classic 3-biomarker panel comprising of ER, PR andHER2, 2) a 5-biomarker panel comprising of the above receptors incombination with traditional basal-like biomarkers, basal CK5/6 andCK14, and 3) a 4-biomarker panel comprising of ER, PR, and HER2, incombination with FOXC1.

The primary objective of this study was to establish whether the FOXC1IHC assay has prognostic value in breast cancer. The secondary objectivewas to compare the prognostic value of molecular subtype models usingsurrogate IHC biomarkers in breast cancer.

Methods

Patients.

Review of a prospectively acquired institutional database identified 904patients with primary infiltrating ductal breast cancer diagnosedbetween Jan. 1, 1995 and Dec. 31, 2004. Patients who were diagnosed withstage IV breast cancer at initial presentation and who did not undergoprimary surgical therapy at John Wayne Cancer Center institution wereexcluded from the analysis.

Translational Study Design.

This translational study was performed with institutional review boardapproval and is reported according to the Reporting Recommendations forTumor Marker Prognostic Studies (REMARK) (McShane et al. 2005).Laboratory personnel, who remained blinded to patient clinical data andoutcomes, performed all IHC assays. Assay results were interpreted andscored by a single pathologist (JMS) who remained blinded to theclinical and pathologic data. The design and statistical plan werefinalized before merging the above generated assay results with theclinical data, prior to performance of data analysis.

Immunohistochemistry Protocols.

A board-certified pathologist fellowship trained in breast pathology(JMS), who remained blinded to the clinical and pathologic data reviewedIHC (ER, PR, HER2) slides selected randomly from each pre-designatedgroup of patients based on receptor status. Approximately 20% of thestudy cohort had such verification of receptor status performed. Thiswas done as an internal quality control measure to ensure that the ER,PR and HER2 status of patients at the time of performance of this studywas in agreement with that initially rendered at the time of initialdiagnosis. No significant difference was encountered in the course ofthis quality control exercise. Biomarker expression status based on IHCassays was scored using criteria from published guidelines. ER and PRstatus were considered positive if immunostaining was seen in >10% oftumor nuclei. HER2 status was considered positive if immunostaining wasscored as 3+ according to HercepTest criteria. For an equivocal result(2+), HER2 status was considered positive if the fluorescent in situhybridization (FISH) assay revealed a HER2: chromosome 17 amplificationratio (Yaziji et al. 2004).

Archival formalin-fixed paraffin embedded (FFPE) tissue blocks forpatients designated to be triple-negative with respect to hormonereceptor status, i.e., for those who were ER⁻/PR⁻/HER2⁻ were thenobtained. Tissue blocks were sectioned into serial 5 μm thick tissuesections and subjected to IHC analysis for CK5/6 (D5 and 1664, CellMarque Corp, Rocklin, C A; no dilution), CK 14 (VP-C410, VectorLaboratories, Burlingame, Calif.; dilution 1:20) and FOXC1 (Ray et al.2010). Semiquantitative analysis was performed by one pathologist (JMS)blinded to clinical and pathological data who scored the intensity ofimmunoreactivity on a scale of 0 (no staining) to 3 (strong staining).CK5/6 and CK14 stains were considered positive if any cytoplasmic and/ormembranous invasive carcinoma cellular staining was observed (Nielsen etal 2004). FOXC1 protein expression status was considered positive onlyif any nuclear staining of tumor cells was observed (Ray et al. 2010).

Immunohistochemcial Definition of Breast Cancer Molecular Subtypes.

For purposes of this study, breast cancer molecular subtypes weredefined utilizing surrogate IHC biomarker panels as has been earlierreported (Nielsen et al. 2004). ER and HER2 status were used to defineluminal (ER⁻/HER2⁻), luminal/HER2⁺ (ER⁺/HER2⁺), HER2⁺ (ER⁻/HER2⁺) andbasal-like (ER⁻/HER2⁻) molecular subtypes. In addition to assessing theprognostic significance of FOXC1 protein expression in breast cancer,the prognostic significance of three separate surrogate IHC biomarkerpanels was also compared and used to define BLBC: 1) the triple negativephenotype or TNP, defining BLBC as being negative for the routinelytested receptor biomarkers ER, PR and HER2, 2) a 5-biomarker panelcomprising of TNP combined with CK5/6 and CK14, defining BLBC as beingnegative for ER, PR and HER2 and positive for either CK5/6 and/or CK14expression, and 3) a 4-biomarker panel comprising of TNP and FOXC1,defining BLBC as being negative for ER, PR and HER2 and positive forFOXC1 protein expression. In the 5-biomarker and 4-biomarker models, thesubset of TNP patients negative for all biomarkers are referred to as5NP and 4NP, respectively.

Statistical Analysis.

All statistical analyses were performed using SAS (version 9.1.3, SAS,Cary, N.C.). Criteria used to determine positive or negative status of aspecific biomarker were determined prior to performing any statisticalanalysis. Analysis of categorical variables was performed using χ² testand Fisher's exact test. The Mann-Whitney U test was employed to comparenon-normal continous variables. For survival analysis, overall survival(OS) was the outcome measure used. Survival time was calculated as thedate of diagnosis until the date of death. Survival times were censoredif the patient was still alive on Oct. 15, 2009 (the last date of updateof the database). Univariate survival curves were generated by theKaplan-Meier method (Bland et al. 1998) and significance determinedusing the log-rank test (Bland J M, Altman D G. The logrank test. BMJ2004; 328:1073). Multivariate analysis was performed using Cox'sproportional hazards analysis. For purposes of evaluating the prognosticsignificance of each of the above IHC biomarker panel definitions ofBLBC, three separate models were constructed for the 3-biomarker,5-biomarker and 4-biomarker definitions of BLBC. The three differentmultivariate models were compared using the likelihood ratio test andAkaike's Information Criterion (AIC) (Akaike 1974). In addition, we allhypotheses were tested using the Wald test (Cox 1974) and associated Pvalue. All tests were two-sided and P values <0.05 were consideredstatistically significant.

Results and Discussion

In this series of 904 patients diagnosed with primary invasive ductaladenocarcinoma of the breast (FIG. 15), all patients had pre-existingdata with regard to IHC detection of ER, PR and HER2 receptor status.Patients who were diagnosed with stage IV breast cancer at initialpresentation (n=19), who did not undergo primary surgical therapy atJohn Wayne Cancer Institute (n=125), were excluded from the analysis.The final sample size of the study cohort was 759.

Clinicopathologic Features of Study Cohort.

Clinicopathologic features of the 759 patients included in this studyappear in Table 7 (below) classified according to ER and HER2 status,approximating the molecular subtypes.

TABLE 7 Clinical and histopathologic characteristics of the patientcohort - T stage and nodal status are based on final pathologicassessment. Luminal Luminal/HER2 HER2 Basal-like (ER⁺/HER2⁻) (ER⁺/HER2⁺)(ER⁻/HER2⁺) (ER⁻/HER2⁻) n = 481 n = 95 n = 57 n = 126 Subtype (63.3%)(12.5%) (7.5%) (16.7%) Age (mean ± SD) 58.3 ± 13.5 52.0 ± 11.7 53.5 ±10.4 56.1 ± 15.2 Tumor size 0-2 cm 356 (74.0) 56 (59.0) 31 (54.4) 68(54.0) 2-5 cm 102 (21.2) 27 (28.4) 17 (29.8) 40 (31.7)  >5 cm 13 (2.7)10 (10.5) 4 (7.0) 15 (11.9) Unknown 10 (2.1) 2 (2.1) 5 (8.8) 3 (2.4)Nodal status Negative 322 (66.9) 56 (58.9) 30 (52.6) 70 (55.6) Positive140 (29.1) 38 (40.0) 26 (45.6) 48 (38.1) Unknown 19 (4.0) 1 (1.1) 1(1.8) 8 (6.3) Tumor grade 1 149 (31.0) 1 (1.0) 0 (0) 1 (0.8) 2 225(46.8) 30 (31.6) 8 (14.0) 11 (8.7) 3 101 (21.0) 62 (65.3) 47 (82.5) 109(86.5) Unknown 6 (1.2) 2 (2.1) 2 (3.5) 5 (4.0) Hormonal therapy No 96(20.0) 16 (16.8) 48 (84.2) 85 (67.5) Yes 328 (68.2) 67 (70.5) 3 (5.3) 9(7.1) Unknown 57 (11.9) 12 (12.5) 6 (10.5) 32 (25.4) Chemotherapy No 238(49.5) 21 (22.1) 8 (14.0) 24 (19.1) Yes 174 (36.2) 69 (72.6) 43 (75.5)67 (53.2) Unknown 69 (14.4) 5 (5.3) 6 (10.5) 35 (27.8) Herceptin therapyNo — 65 (68.4) 34 (59.7) — Yes — 21 (22.1) 14 (24.6) — Unknown — 9 (9.5)9 (15.8) —

As illustrated in Table 7, 63.3% (481 of 759) were defined as havingLuminal (ER⁺/HER2⁻) subtype, 12.5% (95 of 759) as having Luminal/HER2(ER⁻/HER2⁺) subtype, 7.5% (57 of 759) as having HER2 (ER⁻/HER2⁺) subtypeand 16.7% (126 of 759) were defined as being BLBC by the TNP definition(3-biomarker panel). 90 of these 126 specimens underwent additional IHCassays performed for assessment of CK5/6, CK14 and FOXC1. Analyses werenot performed for the 36 remaining specimens because of exhaustion ofinvasive tumor tissue, inadequate remaining invasive tumor in the tissueblock or technical issues. 60 of 90 TNP patients were BLBC by the basalcytokeratin definition (5-biomarker panel), and 55 of 87 TNP patientswere basal-like by the FOXC1 definition (4-biomarker panel).Clinicopathologic features of the TNP patients classified according toeither the 5-biomarker panel or the 4-biomarker panel appear in Table 8below. Representative IHC images of FFPE sections stained with CK5/6,CK14 or FOXC1 are shown in FIG. 26.

TABLE 8 Clinicopathologic characteristics of patient subset with triplenegative breast cancer. Basal CK⁻ Basal CK⁺ FOXC1⁻ FOXC1⁺ n = 38 n = 60n = 42 n = 49 (5.6%) (8.9%) p-value (6.3%) (8.2%) p-value Age 59.7 ±14.4 55.9 ± 16.6 0.2429 63.2 ± 15.2 51.5 ± 14.4 0.0003 (mean ± SD) Tumorsize 0-2 cm 24 (63.2) 25 (41.7) 19 (45.2) 23 (46.9) 2-5 cm 7 (18.4) 26(43.3) 14 (33.3) 19 (38.8)  >5 cm 7 (18.4) 7 (11.7) 8 (19.1) 6 (12.3)Unknown 2 (3.3) 1 (2.4) 1 (2.0) Nodal status Negative 21 (55.3) 32(53.4) 23 (54.7) 26 (53.1) Positive 15 (39.5) 23 (38.3) 13 (31.0) 22(44.9) Unknown 2 (5.3) 5 (8.3) 6 (14.3) 1 (2.0) Tumor grade 1 1 (2.6) 0(0) 0 (0) 0 (0) 2 4 (10.5) 4 (6.7) 6 (14.3) 2 (4.1) 3 32 (84.2) 54(90.0) 35 (83.3) 45 (91.8) Unknown 1 (2.6) 2 (3.3) 1 (2.4) 2 (4.1)Hormonal therapy No 23 (60.5) 39 (65.0) 25 (59.6) 32 (65.3) Yes 4 (10.5)1 (1.7) 3 (7.1) 1 (2.0) Unknown 11 (29.0) 23 (33.3) 14 (33.3) 16 (32.7)Chemotherapy No 9 (23.7) 9 (15.0) 9 (21.4) 5 (10.2) Yes 17 (44.7) 30(50.0) 17 (40.5) 28 (57.1) Unknown 12 (31.6) 21 (35.0) 16 (38.1) 16(32.7) **p value

Prognostic Value of FOXC1 Protein Expression in Breast Cancer.

In the present study, FOXC1 status was considered positive only if anynuclear staining was observed (Ray et al. 2010). Positive expression ofFOXC1 protein was found to be a significant predictor of overallsurvival (FIG. 16) amongst breast cancer patients on univariate analysis(HR 3.364 95% Cl 1.758-6.438, P=0.0002) (Table 9-10). Other standardclinicopathologic factors such as age, tumor size, nodal status andtumor grade were also found to be significant predictors of overallsurvival. Adjuvant treatment variables such as hormonal therapy,chemotherapy or trastuzumab (herceptin) therapy were not significantpredictors of overall survival, indicating equivalent effects across allgroups. Furthermore, the prognostic significance of FOXC1 on univariateanlaysis was retained regardless of the cutoff point used to segregatepatients into FOXC1 positive and FOXC1 negative subsets (Table 9, FIG.16). The prognostic significance of FOXC1 protein expression as anindependent predictor of OS persisted on multivariate analysis, whereasboth the triple negative phenotype as well as the basal cytokeratinpositive phenotypes no longer remained significant on multivariateanalysis (Table 10). Again, the prognostic significance of FOXC1 as anindependent predictor of OS on multivariate analysis was also retainedregardless of the cutoff point used to segregate patients into FOXC1positive and FOXC1 negative subsets. The optimal cutoff point for FOXC1protein expression scored on IHC in this study was 0-1 (n=42) versus 2-3(n=49), although FOXC1 protein expression remained a highly significantprognostic marker at all cutoff points tested (0 versus 1-3, 0-1 versus2-3 and 0-2 versus 3).

TABLE 9 Univariate cox regression analysis of the prognosticsignificance of individual clinicopathologic and treatment variables on5 year overall survival. N P-value Hazard ratio (95% CI) Age 759 <0.00011.046 (1.028 1.064) Tumor Size 739 0.0006 1.826 (1.293 2.580) (>=5,2-4.99, 0-2) Nodal Status 730 0.0113 1.913 (1.158 3.164) (Positive vs.Negative) Tumor Grade (1, 2, 3) 744 0.0313 1.468 (1.035 2.082) ER⁻/HER2⁻vs. others 759 0.0104 2.027 (1.181 3.480) Basal+ vs. others 731 0.00432.572 (1.344 4.919) FOXC1⁺ (1, 2, 3) vs. others 724 0.0014 2.880 (1.5055.510) FOXC1⁺ (2, 3) vs. others 724 0.0002 3.364 (1.758 6.438) FOXC1⁺(3) vs. others 724 0.0012 3.392 (1.618 7.112) Hormone Therapy (yes vs.no) 652 0.1213 0.660 (0.390 1.116) Chemotherapy (yes vs. no) 644 0.25120.733 (0.432 1.245) Herceptin Therapy (yes vs. no) 688 0.6389 1.275(0.462 3.524)

TABLE 10 Multivariate cox regression analysis of the prognosticsignificance of individual clinicopathologic and treatment variables on5 year overall survival. N P-value Hazard ratio (95% CI) Age 670 <0.00011.049 (1.028 1.069) Tumor Size 0.0022 1.797 (1.234 2.618) (>=5, 2-4.99,0-2) Nodal Status (Positive vs. Negative) Tumor Grade (1, 2, 3)ER⁻/HER2⁻ vs. others Basal+ vs. others FOXC1⁺ (1, 2, 3) vs. others*0.0005 3.406 (1.713 6.775) FOXC1⁺ (2, 3) vs. others *0.0001 3.839(1.928 7.645) FOXC1⁺ (3) vs. others *0.0019 3.755 (1.632 8.636) HormoneTherapy (yes vs. no) Chemotherapy (yes vs. no) Herceptin Therapy (yesvs. no)

Overall Survival According to IHC Models of Breast Cancer MolecularSubtype.

The breast cancer subtypes as defined by the surrogate IHC biomarkerpanels differed significantly in predicting OS (FIG. 17). The modelutilizing FOXC1 achieved the most significant degree of prognosticstratification (p<0.0001). In the 3-biomarker panel, the 5-year and10-year OS for BLBC patients (defined using TNP) was 85% and 77%,respectively. In the 5-biomarker panel, the 5-year and 10-year OS forBLBC patients (defined using TNP+CK5/6 and CK14_ was 82% and 66%,respectively. In the 4-biomarker panel, the 5-year and 10-year OS forBLBC patients (defined using TNP+FOXC1) was 77% and 69%, respectively.

On univariate Cox regression analysis, in addition to standardclinicopathologic factors such as age, tumor size, lymph node status andtumor grade, BLBC defined according to the 3-biomarker, 5-biomarker and4-biomarker panels were all significant predictors of breast cancer OS(Table 9, above). On multivariate Cox regression analysis, only age,tumor size and BLBC defined according to the 4-biomarker panel based onFOXC1 protein expression retained significance and were independentpredictors of OS. The 3-biomarker panel utilizing TNP as well as the5-biomarker panel based on basal CK expression lost significance onmultivariate analysis.

For purposes of evaluating the prognostic significance of each of theabove IHC biomarker panel definitions of BLBC, three separatemultivariate models of breast cancer molecular subtypes were constructedfor the 3-biomarker (based on the triple negative phenotype (TNP)),5-biomarker (based on expression of basal cytokeratins) and 4-biomarker(based on protein expression of FOXC1) definitions of BLBC, eachincluding the standard clinicopathologic factors age, tumor size, nodalstatus and tumor grade. The three multivariate models were comparedusing the likelihood ration test and Akaike's Information Criterion(AIC). The 4-biomarker model based on FOXC1 protein expression had thelowest AIC score indicating it to be the model with the greatestprognostic value (Table 11).

TABLE 11 Comparison of the three different multivariate models of breastcancer molecular subtype utilizing surrogate immunohistochemicalbiomarker panels. 3-biomarker (TNP) AIC = 748.576 prognostic model N =702 P-value Hazard Ratio (95% CI) Age <0.0001 1.049 1.029 1.069 TumorSize (>5, 2-4.99, 0-2) 0.0153 1.600 1.094 2.338 Nodal Status (positivevs. negative) Tumor Grade (High-3, 0.0123 1.628 1.111 2.385Intermediate-2, Low-1) ER⁻/HER2⁻ vs. others 5-biomarker (Basalcytokeratin) AIC = 719.774 prognostic model N = 677 P-value Hazard Ratio(95% CI) Age <0.0001 1.042 1.022 1.063 Tumor Size (>5, 2-4.99, 0-2)0.0034 1.765 (1207 2.581) Nodal Status (positive vs. negative) TumorGrade (High-3, Intermediate-2, Low-1) Basal⁺ vs. others 0.01 2.499 1.2455.016 4-biomarker (FOXC1) AIC = 712.989 prognostic model N = 670 P-valueHazard Ratio (95% CI) Age <0.0001 1.045 1.028 1.069 Tumor Size (>5,2-4.99, 0-2) 0.0022 1.797 1.234 2.618 Nodal Status (positive vs.negative) Tumor Grade (High-3, Intermediate-2, Low-1) FOXC1⁺ (2, 3) vs.others <0.0001 3.839 1.928 7.645

In the current study cohort of patients with invasive ductal breastcancer, the basal-like phenotype defined on the basis of positive FOXC1protein expression was superior to the traditionally employed triplenegative phenotype, for purposes of prognostic stratification. Thisdemostrates that being “basal-like” is not synonymous with being“triple-negative.” The IHC definition of the basal-like phenotype basedon the positive expression of FOXC1 protein was also superior tobasal-like phenotype defined by the positive expression of basal CK, forpurposes of prognostic stratification. This represents a significantadvance as, unlike basal CKs, FOXC1 represents a potential candidate forthe targeted personalized therapy of patients with BLBC (Ray et al.2010). FOXC1 not only promises to be a prognostic biomarker, but apredictive biomarker as well—predictive of the therapeutic efficacy ofany future anti-FOXC1 directed drug or biologic for the treatment ofpatients with basal-like breast cancer.

The tissue microarray platform relies on representative core needlesampling of specimens and is an excellent method for exploratoryresearch projects that considerably minimizes resource allocation. It isideal for assessing the presence of biomarkers that are expressedhomogeneously throughout a specimen such as ER and HER2. However, it isnot ideal for assessing the presence of potential biomarkers, such asbasal CKs, that are expressed heterogeneously throughout the tissuesection (refer Laakso et al.). Therefore, entire tissue sections wereused instead of tissue microarrays for the analysis.

The analysis discussed above was restricted to the invasive ductalbreast cancer histologic type. This was done to minimize potentialconfounding effects (prognostic, biologic or both) of histologic subtypeon molecular subtype in breast cancer. However, the above findings withregard to FOXC1 protein expression may be extrapolated to otherhistologic types of breast cancer such as lobular breast cancer.

FOXC1 mRNA expression, is found to have a prognostic impact on OS inbreast cancer that is likely independent of lymph node status, and is atleast in part attributable to a significantly higher rate of associationwith the early occurrence of brain metastasis, often as the first siteof distant metastasis, even in lymph node negative patients. In thepresent study, when FOXC1 protein expression status as assessed by IHCwas included in the multivariate model, nodal status failed to retainsignificance. This lends further support to the prognostic impact ofFOXC1 being independent of nodal involvement.

The 4-biomarker panel utilizing FOXC1 protein expression showed superiorprognostication compared to the 5-biomarker panel utilizing basal CK 5/6and/or CK14 in the current patient cohort (when considered incombination with ER, PR and HER2 status of breast cancer specimens).This suggests that FOXC1 protein expression, when present, is successfulin diagnosing patients possessing the true basal-like molecular subtypefrom amongst patients with the triple-negative phenotype. A subsetanalysis of only triple-negative patients in this study cohort displayeda trend towards supporting this conclusion (data not included).

Example 4: FOXC1 Responsible for Aggressive and Invasive Phenotype,Making it a Viable Therapeutic Target

Materials and Methods

FOXC1-knockdown cells. FOXC1 shRNAs and a control shRNA that does notmatch any known cDNA were from Sigma. Cells were stably transfected withthe FOXC1 or the control shRNA construct and selected with 5 μg/mLpuromycin. Pooled knockdown cells were used for experiments.

FOXC1 shRNAs.

The following shRNAs were purchased from Sigma:

Mouse FOXC1 shRNA Sequences:

CCGGGAGCAGAGCTACTATCGCGCTCTCGAGAGCGCGATAGTAGCTCTGCTCTTTTG (shRNA1; SEQ ID NO:5);  andCCGGTGGGAATAGTAGCTGTCAGATCTCGAGATCTGACAGCTACTATTCCCATTTTTG (shRNA2; SEQ ID NO:6).Human FOXC1 shRNA Sequences:

CCGGCAAGAAGAAGGACGCGGTGAACTCGAGTTCACCGCGTCCTTCTTCTTGTTTTTG (shRNA1; SEQ ID NO: 7);  andCCGGCCCGGACAAGAAGATCACCCTCTCGAGAGGGTGATCTTCTTGTCCGGGTTTTT (shRNA2; SEQ ID NO: 8).Control shRNA (does not Target any Known Human or Mouse Gene):

(SEQ ID NO: 9) CCGGCAACAAGATGAAGAGCACCAACTCGAGTTGGTGCTCTTCATCTTGTTGTTTTT 

FOXC1-Overexpressing Cells.

A full-length human FOXC1 cDNA was stably transduced into breast cancercells. Stable cell lines were selected with 800 μg/mL G418. Pooledpopulations were used for experiments.

Cell culture. Cancer cell lines were from American Type CultureCollection. Normal human mammary epithelial cells (HMEC) were fromClonetics. Cell proliferation was assessed by the MTT assay.Three-dimensional cell culture was done using BD Matrigel matrix in96-well plates.

Cell Migration and Invasion Assay.

Briefly, 10⁴ cells were plated on the top of a Boyden chamber insertswith an 8 μm pore size. The inserts were then transferred into a 24-wellplate. Each well contained DMEM with 10% serum as the chemoattractant.To rule out the effect of cell proliferation, 2 μg/ml mitomycin C wasadded to the cells. After incubation, cells remaining on the uppersurface of the chambers were removed with cotton swabs. Cells on thelower surface of the inserts were stained with the HEMA3 kit (Fisher).The membrane was then mounted onto a microscope slide and the migratingcells were counted in 5 different areas using a light microscope. Forinvasion assays, inserts were coated with a thin layer of Matrigelbasement membrane matrix (BD Biosciences) and the same procedures werefollowed.

Immunohistochemistry and Immunoblotting were performed as describedabove.

Results and Discussion

The function of FOXC1 in breast cancer cells was examined.Overexpression of FOXC1 in MDA-MB-231 BLBC cells (harboring moderatelevels of endogenous FOXC1) increased cell proliferation, migration, andinvasion (FIG. 11A). Similar results were observed in MCF-7 luminalbreast cancer cells (harboring undetectable levels of endogenous FOXC1;FIG. 12A). FOXC1 overexpression also enhanced anchorage-independentgrowth of MCF-7 cells in soft agar. Immunoblotting indicated that cyclinD1, fibroblast markers (vimentin, fibronectin, and α-smooth muscleactin), integrins β4 and β1, and matrix metalloproteinases MMP2 and MMP9were upregulated by FOXC1 overexpression (FIG. 12B-D). FOXC1 has beenshown to induce epithelial-mesenchymal transition (EMT) in MCF-12Amammary epithelial cells (Bloushtain-Qimron et al. 2008-21). Similarly,FOXC1 overexpression in MCF-10A mammary epithelial cells induced amesenchymal phenotype accompanied by increased expression of the basalmarker P-cadherin and decreased expression of the epithelial markerE-cadherin (FIG. 12E). Regulation of these genes by FOXC1 was alsoconfirmed by quantitative reverse transcription-PCR (data not shown).These data suggest that FOXC1 can elicit an aggressive phenotypeassociated with BLBC cells.

To assess the effects of FOXC1 depletion, FOXC1 shRNA was stablytransduced into 4T1 mouse breast cancer cells, which are a model forstage IV human breast cancer (Aslakson & Miller 1992-22) and possesshigh levels of endogenous FOXC1 (FIG. 13A). These shRNAs reduced FOXC1levels by >90% (FIG. 13B) and decreased cell proliferation, migration,and invasion (FIG. 11B). Similar results were obtained with BT549 humanbreast cancer cells when FOXC1 was reduced by shRNA (FIGS. 13C and D).FOXC1 depletion also converted 4T1 cells from fibroblast-like toepithelial-like and suppressed cell growth in three-dimensional cultureand colony formation in soft agar (FIGS. 11C and D). These data furthersuggest a role of FOXC1 in regulation of cell function. Studies havesuggested that BLBC may possess extraordinarily high growth rates(Seewaldt & Scott 2007) and an EMT phenotype (Sarrio et al. 2008)compared with other breast cancer subgroups. FOXC1 may play a role incoordinating these BLBC properties. Further, DNA methylation may play arole in BLBC-associated FOXC1 expression. In summary, these studiessupport FOXC1 as a theranostic biomarker, i.e., a diagnostic andprognostic biomarker as well as a therapeutic target.

Example 5: FOXC1 Regulation of ERα Expression and Function

Based on the studies below, it was found that FOXC1 induces NF-κBsignaling to inhibit ERα expression. This study provides a molecularbasis for the ERα-negative phenotype of basal-like breast cancer andalso provides implications for the role of FOXC1 in the response ofbreast cancer cells to antiestrogen treatment.

Materials and Methods

Cell Culture.

MCF-7 and T47D human breast cancer cell lines were obtained from theBreast Center at Baylor College of Medicine. Cells were routinelymaintained in Dulbecco's modified Eagle's medium (DMEM) supplementedwith 10% fetal bovine serum (FBS), 2 mM glutamine, 50 IU/ml ofpenicillin, 50 μg/ml of streptomycin, and 10 μg/ml insulin. Cells werekept at 37° C. in a humidified incubator with 5% CO₂. Tamoxifen and17β-estradiol were from Sigma (St Louis, Mo.). The IKK small moleculeinhibitor BMS-345541 was purchased from Calbiochem (Gibbstown, N.J.).For experiments involving estradiol and tamoxifen, cells wereserum-starved overnight and then stimulated with the ER ligands fordifferent time periods prior to cell proliferation assays.

Microarray Data Analysis.

Raw expression data from publicly available human breast cancer geneexpression microarray data sets (Ginestier et al., 2006; Lu et al.,2008; Perou et al., 2000; Pollack et al., 2002; Richardson et al., 2006;Schuetz et al., 2006; Sorlie et al., 2001; Sorlie et al., 2003; Zhao etal., 2004) and the Oncology—Breast Samples Project database (Bittnet etal.) of the International Genomics Consortium (IGC) athttps://expo.intaen.ora/expo/public were analyzed using Oncomine 4.0software.

Stable Transfection.

MCF-7 and T47D cells were stably transfected for 24 h with a FLAG-taggedFOXC1 construct or the empty vector using Lipofectamine 2000 reagent(Invitrogen). Stable clones were then selected using 800 μg/ml G418(Invitrogen). Expression of FLAG-FOXC1 was verified by western blottingwith an anti-FOXC1 antibody (Santa Cruz Biotechnology, Santa Cruz,Calif.) and an anti-FLAG antibody (Origene, Rockville, Md.).

Transient Transfection.

MCF-7 cells were grown for 48 h till 80% confluence before transfection.For cotransfections, 0.1 μg DNA of ERE-tk-luc or NF-κB-luc (Promega,Madison, Wis.) reporter construct and 1 μg of FLAG-FOXC1 or NF-κB p65vector was added to 60 mm dishes. The transfected cells were culturedfor 24 h. The estrogen-responsive reporter plasmid ERE-tk-luc contains asingle consensus ERE upstream of a minimal thymidine kinase promoter andthe luciferase gene (Cui et al., 2003). At 24 h after transfection,cells were washed twice with PBS and harvested in 200 μl of reporterlysis buffer (Promega). Twenty nanograms of a β-galactosidase expressionvector pSV-β-Gal (Promega) were co-transfected as an internal control.Luciferase and β-galactosidase assays were performed using Promegareporter assay reagents and the GloMax Multi-detection system. To testwhether p65 overexpression inhibits ERα expression, MCF-7 cells weretransfected with a p65 construct or the vector for 48 h, followed byimmunoblotting.

Immunoblot Analysis.

Whole cell lysates for western blotting were generated by cell lysisbuffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 2 mM EDTA, 1% NP-40, 10%glycerol) supplemented with a protease inhibitor cocktail (Sigma, StLouis, Mo.). Equal amounts of protein were separated by 10% SDS-PAGE andthen transferred onto a nitrocellulose membrane. The remaining stepswere conducted according to a standard immunoblotting protocol (Qu etal., 2009). Immunoblotting was done with polyclonal antibodies againstp65, FOXC1, IRS1 (1:200; Santa Cruz Biotechnology), polyclonalantibodies against phospho-p65, p50, IκBα (1:1000; Cell Signal), ormonoclonal antibodies against ERα (1:500; Novocastra Laboratories,Newcastle upon Tyne, UK), PR (1:500; DAKO, Carpinteria, Calif.). Anti-βactin (Sigma) was used at a 1:10000 dilution. After the primary antibodyincubation, the membrane was again washed with PBST three times (5 mineach) and then incubated with a horseradish peroxidase (HRP)-linkedsecondary antibody (Amersham, Piscataway, N.J.) at a dilution of 1:4000in blocking solution. The membrane was washed and bands were visualizedusing chemiluminescence assays.

Real-time reverse transcription-PCR. Total RNA was isolated from breastcancer cells using the RNeasy mini kit (Qiagen, Valencia, Calif.). PCRamplification was performed by using Rotor-Gene 3000 Real Time PCRSystem (CoRbett Research) in a 25-NL reaction volume. The PCR mixturecontained SuperScript® III Reverse Transcriptase, TaqMan probe, andforward and reverse primers. Samples were incubated for 1 cycle at 95°C. for 2 min, 40 cycles at 95° C. for 30 s, and 60° C. for 60 s. Allsamples were run in triplicate. Results were analyzed by using theRotor-Gene 3000 software package (Corbett Research). Primer informationis as follows: FOXC1 forward primer 5′-CGGTATCC AGCCAGTCTCTGTACCG-3′(SEQ ID NO:10), FOXC1 reverse primer 5′-GTTCGGCTTTGAGGGTGTGTC-3′ (SEQ IDNO:11), ERα forward primer 5′-CGGTTAGATTCATCATGCGGAACCG-3′ (SEQ IDNO:12), and ERα reverse primer 5′-TGTGTAGAGGGCATGGTGGAG-3′ (SEQ IDNO:13). ERα and FOXC1 mRNA data were normalized by the β-actin mRNAvalue.

Immunofluorescence staining. MCF-7 cells were transiently transfectedwith GFP-FOXC1 plasmid for 24 h. Then the cells were digested withtrypsin and seeded in chamber slides (BD Biosciences, Franklin Lakes,N.J.). After 12-h incubation, cells were fixed with 4% formaldehyde andthen permeabilized with PBS containing 0.1% Triton X-100. Slides wereblocked by 5% BSA for 30 minutes and incubated with a primary anti-ERαantibody (1:100) at room temperature for 1 h. Cells were then incubatedwith an Alexa Fluor 546—conjugated secondary antibody (1:200,Invitrogen) for 30 min. Slides were washed by PBS three times for 5minutes each, mounted in DAPI, and observed under a high resolutionNikon TI-E microscope.

IPA Signaling Pathway Analysis.

The Richardson et al. data set (Richardson et al., 2006) was subjectedto Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Redwood City,Calif.). Briefly, global gene expression profiles of all breast cancersamples were analyzed according to their molecular subgroup (basal-like,HER2 and luminal) with respect to their association with a specificcanonical pathway in the Ingenuity Pathways Knowledge Base. Thesignificance of the association between the average global geneexpression profile associated with a particular subgroup and thespecific canonical pathway was measured in two ways: 1) A ratio of theaverage number of genes from a particular subgroup that map to thepathway divided by the total number of genes (having proberepresentation on the microarray platform) assigned to the canonicalpathway was calculated. 2) Fischer's exact test was used to calculate ap-value determining the probability that the association between thegenes in any particular subgroup and the canonical pathway is explainedby chance alone. The negative log of this p-value is the Impact Factor.

NF-κB Transcription Factor TransAM Assay.

NF-κB family activity was measured using the TransAM NF-κB ELISA kit(Active Motif, Carlsbad, Calif.) according to the manufacturer'sinstructions. Briefly, isolated nuclear pellets were resuspended inextraction buffer (20 mM Hepes pH 7.9, 0.4 M NaCl, 1 mM EDTA).Supernatant (nuclear extract) was retained after a secondcentrifugation. Samples (10 μg) were added in triplicate to 96-wellplates coated with an oligonucleotide that contains a consensus bindingsequence for NF-κB components. After 1 h incubation at room temperature,primary antibodies of distinct NF-κB components were added; subsequentaddition of HRP-conjugated secondary antibody produced a sensitivecolorimetric readout quantified by spectrophotometry at the 450-nmwavelength with a reference wavelength of 655 nm.

Cell Proliferation Assay.

Cell viability was assessed by the3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay. Cellswere seeded in 24-well plates at 30% confluence and the MTT assay wasperformed one, two, three and four days after treatment. For each assay,50 μl of MTT (5 mg/ml) were added to each well and cells were incubatedat 37° C. for an additional 4 h. After centrifugation, the supernatantwas carefully aspirated and 300 μl of DMSO (Sigma) were added to eachwell. Immediately after resolubilization, all plates were scanned at 575nm on a microplate reader. The absorbance (A) value represented thenumber of live cells.

Chromatin Immunoprecipitation (ChIP) Assay.

ChIP assays were performed by using a CHIP-IT Express Enzymatic kit(Active Motif) according to the manufacturer's protocol. Cells weregrown to 80% confluence in DMEM supplemented with 10% FBS and thencross-linked with 1% formaldehyde at room temperature for 10 min. Cellswere harvested and digested with trypsin, followed by centrifugation.Supernatants were precleared at 4° C. for 30 min with salmon spermDNA-protein A-Sepharose and immunoprecipitated with an anti-p65 antibody(Santa Cruz Biotechnology) overnight at 4° C. Immunoprecipitation withnormal rabbit IgG was performed to evaluate the presence of non-specificinteractions, and aliquots of DNA-protein complexes were analyzed by PCRto normalize for DNA input. Immunocomplexes were incubated with salmonsperm DNA-protein A Sepharose for 1 h at 48° C. Pellets were washed andeluted as per the manufacturer's instructions and then incubatedovernight at 65° C. DNA fragments were purified with a QIAquick Spin Kit(Qiagen, Valencia, Calif.). The primers used for the ChIP assays are asfollows: ERα forward primer, 5′-AGAAGCTAGACCTCTGCAGG-3′ (SEQ ID NO:14),and ERα reverse primer, 5′-AAGCAG GGGCAAGGAAATATC-3′ (SEQ ID NO:15). Theamplified 140-bp fragment spans a conserved p65 binding site GGGACTTTCTin the F promoter. For PCR, 2 μl from a 30-μl DNA extraction and 30cycles of amplification were used.

Statistical Analysis.

The results are presented as mean±standard deviation (SD) of samplesmeasured in triplicate or duplicate. Each experiment was repeated threetimes, unless otherwise indicated. The Student's t-test was used tocalculate differences between the various experimental groups. Thedifference was considered statistically significant with P<0.05.

Results and Discussion

FOXC1 is Associated with ERα-Negative Human Breast Cancer.

FOXC1 has been identified as a pivotal marker for basal-like breastcancer (Ray et al., 2010), which is characterized by low or absentexpression of ER, PR, and HER-2/neu. Analysis of the Oncomine database,which provides publicly available gene expression profiling datasets onhuman cancers, revealed that FOXC1 mRNA levels inversely correlated withERα expression in multiple breast cancer cDNA microarray array data sets(Ginestier et al., 2006; Lu et al., 2008; Perou et al., 2000; Pollack etal., 2002; Richardson et al., 2006; Schuetz et al., 2006; Sorlie et al.,2001; Sorlie et al., 2003; Zhao et al., 2004) (FIGS. 18A and 19). Next,FOXC1 levels in well-known ERα-positive or -negative human breast cancercell lines were examined. Immunoblotting demonstrated that FOXC1 wasreadily detected in ERα-negative breast cancer cell lines, but not inERα-positive cells (FIG. 18B).

FOXC1 Downregulates ERα Expression.

In light of the strong inverse correlation between FOXC1 and ER levelsin breast cancer, it was determined whether FOXC1 affects ERαexpression. To address this question, FOXC1 was stably transfected intoERα-positive MCF-7 breast cancer cells. Ectopic overexpression of FOXC1substantially reduced ERα levels in stable transfectants, as shown byreverse transcription-PCR (RT-PCR) and western blotting (FIGS. 20A and20B). In accordance, well-established estrogen-regulated genes PR andinsulin receptor substrate-1 (IRS-1) were also downregulated inFOXC1-overexpressing MCF-7 cells (FIG. 20B). Similar results were alsoobserved in ERα-positive T47D breast cancer cells (FIG. 21).

To corroborate the above finding, a GFP-FOXC1 fusion gene construct wastransiently transfected into MCF-7 cells. Immunofluorescence stainingdemonstrated that ERα levels were markedly lower in MCF-7 cellsexpressing GFP-FOXC1 compared with neighboring cells harboring barelydetectable GFP signal (FIG. 20C). Next, MCF-7 cells with an estrogenresponse element (ERE)-luciferase reporter construct were transientlyco-transfected as described previously (Cui et al., 2003), and a FOXC1plasmid, and then stimulated the cells with estradiol. As illustrated inFIG. 20D, FOXC1 suppressed estradiol-induced luciferase activity,suggesting that the transcriptional activity of ERα was inhibited. Takentogether, these results indicate that FOXC1 is a repressor of ERαexpression and thereby its activity.

FOXC1 Reduces the Sensitivity of Breast Cancer Cells to ERα Ligands.

Previously, FOXC1 overexpression was shown to enhance cell growth undernormal culture conditions (Ray et al., 2010). Thus, it was determinedwhether FOXC1 affects the growth of MCF-7 cells under other cultureconditions. As illustrated in FIGS. 22A and 22B, FOXC1 overexpressionpotentiated the growth of MCF-7 cells in serum-free medium, butdiminished the increase of cell growth induced by estradiol treatmentcompared with serum-starved conditions. In addition, FOXC1overexpression rendered MCF-7 cells less sensitive to the treatment ofthe antiestrogen tamoxifen (FIG. 22C). Collectively, these data suggestthat the downregulation of ERα by FOXC1 enables MCF-7 cell growth to beless dependent on E2-induced ERα activation or tamoxifen-induced ERαinactivation.

FOXC1 Upregulates NF-κB Activity.

Because analysis of the human ERα gene promoter (Kos et al., 2001;Tanimoto et al., 1999) did not find conserved FOXC1-binding sites, itwas postulated that the inhibition of ERα by FOXC1 may be mediated byother signaling mechanisms. With this in view, an unbiased screeningapproach was adopted. As FOXC1 is an important marker for basal-likebreast cancer, a systematic signaling network analysis of breast cancercDNA microarray data sets was conducted using the Ingenuity IPA platform(see Materials and Methods) to identify basal-like breastcancer-associated signaling pathways. As illustrated in FIG. 23A, NF-κBwas uncovered as one of the most distinctive pathways in the basal-likesubtype, which is consistent with the previous finding that the NF-κBtranscription factor is constitutively activated in ER-negative breastcancer and essential for the proliferation of basal-like breast cancercells (Karin et al., 2002; Nakshatri et al., 1997; Singh et al., 2007).

Given the above finding, it was determined whether FOXC1 regulates NF-κBfunction. Immunoblotting showed that the p65 subunit and p-p65 (Ser546,an IκB kinase [IKK] phosphorylation site) were markedly induced by FOXC1overexpression in MCF-7 cells (FIG. 23B). Conversely, knockdown of FOXC1by its shRNA repressed p65 expression in 411 mouse breast cancer cells,which possess high levels of endogenous FOXC1 (FIG. 23C). Previously itwas shown that p65 levels are primarily controlled at the proteinstability level (Ryo et al., 2003). Using RT-PCR and the proteintranslation inhibitor cycloheximide, this p65 upregulation by FOXC1 wasconfirmed to be via an increase in its protein stability (data notshown). Immunoblotting using nuclear extracts indicated that FOXC1promoted p65 translocation into the nucleus (FIG. 23D). In agreement,TransAM ELISA using oligonucleotides comprising consensus NF-κB-bindingsequences showed that FOXC1 considerably increased the DNA-bindingactivity of p65 (FIG. 23E). To corroborate that FOXC1 enhances NF-κBactivity, an NF-κB-responsive luciferase reporter construct was used. Asillustrated in FIG. 23F, FOXC1 overexpression significantly increasedNF-κB-driven luciferase activity. Co-expression of a super-repressorIκBα, a p65-inhibiting protein, abolished this FOXC1 effect.Interestingly, FOXC1 overexpression sensitized MCF-7 cells topharmacologic inhibition of NF-κB by its small-molecule inhibitorBMS-345541 in cell proliferation assays (FIG. 23G). Similar results wereobtained with other ERα-positive breast cancer cell lines (data notshown). Taken together, these results demonstrate that FOXC1 is a potentinducer of NF-κB activation.

NF-κB downregulates ERα expression. NF-κB is associated with ERαnegative status in breast cancer (Biswas et al., 2004; Nakshatri et al.,1997). It has been shown that NF-κB negatively regulates ERα expression(Biswas et al., 2005; Holloway et al., 2004). To further investigatewhether NF-κB plays a role in the effect of FOXC1 on ERα expression, theNF-κB p65 subunit in MCF-7 cells was overexpressed by transfection.Immunoblotting showed that increased p65 expression lowered ERα proteinlevels in MCF-7 cells (FIG. 24A). Real-time RT-PCR indicated that ERαmRNA levels were also decreased (FIG. 25). Conversely, inhibition ofNF-κB by the IKK inhibitor BMS-345541 in FOXC1-overexpressing MCF-7cells elevated levels of ERα, PR, and IRS-1 (FIG. 24B). In addition,when p65 or ERα was transiently co-transfected with a ERE luciferasereporter construct, E2-induced luciferase activity was substantiallydecreased by p65 co-transfection, while increased by ERα co-transfection(FIG. 24C).

The human ERα mRNA is transcribed from at least seven differentpromoters with unique 5′-untranslated regions (Kos et al., 2001). Allthese ERα transcripts utilize a same splice accept site at nucleotide+163 from the transcription start site in the originally identified exon1 (Green et al., 1986). Previous studies showed that p65 binds to the Bpromoter of the ERα gene (Tanimoto et al., 1999). Notably, there is alsoa highly conserved p65-binding site GGGACTTTCA at position −430 in theERα F promoter (Mahmoodzadeh et al., 2009). To confirm that p65 binds tothis promoter region, chromatin immunoprecipitation (ChIP) assays wereperformed using cell extracts prepared from control andFOXC1-overexpressing MCF-7 cells. The 140 bp amplified promoter regionspans the binding site. As presented in FIG. 24D, p65 binding to the ERαF promoter was increased by FOXC1 overexpression. Taken together, theseresults suggest that p65 mediates the FOXC1 suppression of ERexpression.

In this study, it was shown that expression of FOXC1, a transcriptionfactor essential for mesoderm tissue development in vertebrates (Berryet al., 2002; Saleem et al., 2003) and a marker for basal-like breastcancer (Ray et al., 2010), inversely correlates with levels of ERα inbreast cancer tissues and cell lines. Specifically, it was found thatFOXC1 upregulates the NF-κB p65 subunit, which then downregulates ERαexpression via a transcriptional mechanism. Upregulation of p65 alsodesensitizes breast cancer cells to estradiol and the antiestrogentamoxifen. Essentially, FOXC1 overexpression causes cells to switch fromestrogen-dependent to NF-κB-dependent proliferation, a finding confirmedby breast cancer cell sensitivity to NF-κB inhibition. NF-κB is awell-established transcription factor that plays a central role inregulating the expression of many genes associated with cellproliferation, immune response, inflammation, cell survival, andoncogenesis (Karin et al., 2002; Lin et al.). This study providesevidence for NF-κB-mediated crosstalk between ERα and FOXC1.

Previous studies have revealed that forkhead box A1 (FOXA1) and GATAbinding protein 3 (GATA-3) are expressed in close association with ERα(Albergaria et al., 2009). Both are transcription factors implicated inERα-mediated action in breast cancer (Eeckhoute et al., 2007; Lupien etal., 2008; van der Heul-Nieuwenhuijsen et al., 2009). FOXA1 binds tochromatin DNA and opens the chromatin structure, thereby enhancing thebinding of ERα to the promoters of its target genes. The binding site ofFOXA1 is usually in close proximity to ERα binding sites. In thisregard, FOXA1 acts as a priming factor in the recruitment of ERα to itscis-regulatory elements in the genome and subsequent transcriptionalinduction of target genes such as cyclin D1 in breast cancer cells(Carroll et al., 2005; Laganiere et al., 2005). GATA-3 is required forestrogen stimulation of cell cycle progression in breast cancer cells.It upregulates ERα by binding to two cis-regulatory elements locatedwithin the ERα gene; this binding allows recruitment of RNA polymeraseII to ERα promoters (Eeckhoute et al., 2007). Another forkhead boxtranscription factor FOXO3a also induces ERα expression via binding tothe ERα promoter (Belguise and Sonenshein, 2007; Guo and Sonenshein,2004).

In addition to its association with ERα-negative breast cancer ingeneral, NF-κB activation has been linked to EGFR or HER-2overexpression-induced loss of ER in inflammatory breast cancer (VanLaere et al., 2007). This is consistent with an earlier finding thatNF-κB mediates the downregulation of ER by hyperactive MAPK (Holloway etal., 2004; Oh et al., 2001), commonly induced by EGFR and HER-2overexpression. It should be noted that mechanisms for the inhibition ofERα by NF-κB are still not well understood. NF-κB 05 may act by directlybinding to the ERα promoter (Mahmoodzadeh et al., 2009; Tanimoto et al.,1999). In addition to the reported NF-κB binding sites in the B promoterof the ERα gene (Tanimoto et al., 1999), there is a highly conservedNF-κB binding site in the F promoter of ERα at nucleotides −380 to −420(Mahmoodzadeh et al., 2009). CHIP analysis confirmed that NF-κB can bindto the region containing the conserved sequences. Another possibility isthat p65 interacts with ERα and thereby inhibits ERα activity (Gionet etal., 2009; Stein and Yang, 1995). This may in turn reduce ERαtranscription, which can be positively regulated by estrogen-activatedERα itself through half EREs in its promoter (Piva et al., 1988;Treilleux et al., 1997). The NF-κB effect may also be explained in partby its regulation of genes that modulate ERα expression.

In summary, this study delineates a mechanism for the low or absent ERαexpression in basal-like breast cancer and proposes a new paradigm forinvestigating the control of ERα expression during breast cancerprogression. These findings build on a previous report that expressionof cyclin D1 and other growth-promoting genes is increased inFOXC1-overexpressing cells. A link between ERα and FOXC1/NF-κB may haveclinical implications for ERα-positive breast cancer patients who recurwith ERα-negative cancer.

Example 6: Prognostic Stratification of HER2-Enriched Patients UtilizingSemi-Quantitative Gene Expression Microarray Assessment of FOXC1

Human epidermal growth factor receptor 2 (HER2) enriched tumors displayeither gene amplification or protein overexpression. This subtype ofbreast cancer is notable for its variable prognosis and response tosystemic therapy. It has been suggested that a subset of HER2-positivetumors exhibit basal-like characteristics, the so-called basal-HER2subtype. The basal-HER2 subtype has been shown to have the worstprognosis within HER2-overexpressing tumors. It has been suggested thatthe basal-HER2 subtype simultaneously co-expresses HER2 and markerstypical of basal-like breast cancer. As described in the examples above,FOXC1 is a theranostic biomarker of the basal-like breast cancermolecular subtype. Therefore, FOXC1 expression may be investigatedwithin HER2-overexpressing tumors to determine whether FOXC1 expressionrepresents the basal-HER2 subtype and prognosticates poor overallsurvival (OS).

Gene expression microarray data from 58 HER2-amplified tumors wereobtained from a publicly available database that contained linkedclinical outcomes data (J Clin Oncol. 2010 April 10; 28(11):1813-20.Epub 2010 Mar. 15). The data was analyzed for FOXC1 expression and amedian cutoff value (50th percentile) was used to segregate tumors intoFOXC1 high and FOXC1 low designations. Prognostic significance of FOXC1(high vs. low) was assessed using univariate and multivariate analyses.

FIG. 14 shows that the FOXC1 high designation had a significantly worseOS compared to the FOXC1 low designation (p=0.0313). FOXC1 highdesignation was an independent prognostic indicator for worse OS whencontrolled for age, tumor size, and lymph node status (HR 2.54, 95% Cl1.21-5.33, p=0.0138).

Tumors that co-express FOXC1 and HER2 may represent the hybridbasal-like/HER2+ subtype. Patients with HER2-enriched tumors that havean elevated FOXC1 expression display worse survival. Assessment of FOXC1expression within HER2-enriched tumors may represent a pragmaticapproach for the diagnosis and prognosis of the basal-HER2 subtype.

Example 7: Prognostic Stratification of Luminal Patients UtilizingSemi-Quantitative Gene Expression Microarray Assessment of FOXC1

Estrogen receptor and/or progesterone receptor-enriched tumors displayeither gene amplification or protein overexpression of ER and/or PR. Asubset of ER-positive and/or PR positive tumors may exhibit basal-likecharacteristics, the so-called hybrid basal-like/luminal subtype. Thehybrid basal-like/luminal subtype likely has the worst prognosis withinER or PR overexpressing tumors. The hybrid basal-like/luminal subtypelikely simultaneously co-expresses ER and/or PR and markers typical ofbasal-like breast cancer. As described in the examples above, FOXC1 is atheranostic biomarker of the basal-like breast cancer molecular subtype.Therefore, FOXC1 expression may be investigated within ER and/or PRoverexpressing tumors to determine whether FOXC1 expression representsthe hybrid basal-like/luminal subtype and prognosticates poor overallsurvival (OS).

Gene expression microarray data from ER and/or PR amplified tumors maybe obtained from a publicly available database that contains linkedclinical outcomes data. The data may be analyzed for FOXC1 expressionand a median cutoff value should be used to segregate tumors into FOXC1high and FOXC1 low designations. Prognostic significance of FOXC1 (highvs. low) may be assessed using univariate and multivariate analyses.

FOXC1 high designation likely has a significantly worse OS compared tothe FOXC1 low designation within the luminal subtype. FOXC1 highdesignation is likely an independent prognostic indicator for worse OSwhen controlled for age, tumor size, and lymph node status.

Tumors that co-express FOXC1 and ER and/or PR may represent the hybridbasal-like/luminal subtype. Patients with ER and/or PR enriched tumorsthat have an elevated FOXC1 expression are likely to display worsesurvival. Assessment of FOXC1 expression within ER and/or PR enrichedtumors may represent a pragmatic approach for the diagnosis andprognosis of the hybrid basal-like/luminal subtype.

Example 8: Prognostic Stratification of Triple Negative PatientsUtilizing Semi-Quantitative Gene Expression Microarray Assessment ofFOXC1

Triple negative tumors do not express ER, PR or HER2. A subset oftriple-negative tumors may exhibit basal-like characteristics, theso-called hybrid basal-like/triple negative subtype. The hybridbasal-like/triple negative subtype is associated with the worstprognosis within triple negative tumors. The hybrid basal-like/triplenegative subtype likely expresses markers typical of basal-like breastcancer. As described in the examples above, FOXC1 is a theranosticbiomarker of the basal-like breast cancer molecular subtype. Therefore,FOXC1 expression may be investigated within triple negative tumors todetermine whether FOXC1 expression represents the hybridbasal-like/triple negative subtype and prognosticates poor overallsurvival (OS).

Gene expression microarray data from triple negative tumors may beobtained from a publicly available database that contains linkedclinical outcomes data. The data may be analyzed for FOXC1 expressionand a median cutoff value should be used to segregate tumors into FOXC1high and FOXC1 low designations. Prognostic significance of FOXC1 (highvs. low) may be assessed using univariate and multivariate analyses.

FOXC1 high designation likely has a significantly worse OS compared tothe FOXC1 low designation within the triple negative subtype. FOXC1 highdesignation is likely an independent prognostic indicator for worse OSwhen controlled for age, tumor size, and lymph node status.

Tumors that express FOXC1 and not ER, PR and HER2 may represent thehybrid basal-like/triple negative subtype. Patients with triple negativetumors that have an elevated FOXC1 expression are likely to displayworse survival. Assessment of FOXC1 expression within triple negativetumors may represent a pragmatic approach for the diagnosis andprognosis of the hybrid basal-like/luminal subtype.

Example 1: FOXC1/FOXA1 Transcriptional Balance in Breast Cancer: FromAcquisition of Mesenchymal and Stem Cell Traits to Occult Lymph NodeIndependent Breast Cancer Metastasis

RNA-Seq profiling of the Harvey-Ras (HRAS)-transformed MCF10A cellseries, a well characterized and widely accepted in vitro model ofbreast cancer progression and metastasis (see Wang et al., 1997), wasused to correlate measured FOXC1/FOXA1 ratios to dynamic shifts in EMTmarker expression in 3D matrigel cultures and to stem cell traitsobserved in primary and secondary mammosphere suspension cultures (seeFIGS. 27-33). The ability of the FOXC1/FOXA1 expression ratio wasfurther tested to predict lymph node independent breast cancermetastasis and death in independent human breast cancer gene expressiondatasets (see FIGS. 34-36).

RNA-Seq and qRT-PCR profiling confirmed progressive increase inFOXC1/FOXA1 ratio to correlate with a progressive loss of E-cadherinexpression and synchronous gain of EMT markers N-cadherin, Fibronectin,and Vimentin (see FIGS. 28-30). FOXC1/FOXA1 ratio was found to bedirectly proportional to mammosphere formation efficiency, a surrogateindicator of stem cell enrichment (see FIGS. 31-33). In patients withoutany evidence of nodal metastasis, elevated FOXC1/FOXA1 ratio wasassociated with significantly decreased 10 year Overall Survival (HR2.58; 95% Cl 1.39 to 4.80, p=0.003, 295 patient Van de Vijver dataset;van't Veer et al., 2002), 10 year Disease-specific Survival (HR 1.74;95% Cl 1.16 to 2.61, p=0.008, 1992 patient Curtis dataset; Curtis etal., 2012)) and predicted the development of lung metastasis.

As shown by the results presented herein, an elevated FOXC1/FOXA1expression ratio indicated EMT program activation in breast cancer.Elevated FOXC1/FOXA1 expression ratio also indicated the associatedoccurrence of lymph-node-independent distant metastasis and death inhuman patients. This discovery allows for the early (pre-symptomatic)diagnosis of clinically occult (node negative metastasis by using theFOX/C1 FOXA1 ratio as a biomarker of early breast cancer metastasis.

Example 1: Proteasome Inhibitor Bortezomib Inhibits NF-κB andEffectively Overcomes Cancer Stem Cell Escape Triggered by Wnt InhibitorTherapy in FOXC1 Positive Basal-Like/Claudin-Low Breast Cancer

The Wnt/β catenin signaling pathway is active in basal-like/claudin-lowbreast cancers (Scheel et al., 2001). It was previously shown that FOXC1plays a critical role in mediating aggressive cell traits in suchcancers (see FIGS. 39 and 40; Ray et al, 2010). As provided herein, thelink between Wnt signaling and FOXC1 and its potential in regulatingcancer stem cell (CSC) biology in basal-like/claudin-low breast cancerwas investigated.

The proposed mechanism of FOXC1 regulation by Wnt/β catenin signaling isshown in FIG. 41. FIG. 42 shows that FOXC1 acts as a downstream mediatorof Wnt-driven regulation of CSCs in basal-like/claudin-low breastcancer.

Beta-catenin (CTNNB1) expression level and survival was analyzed using abreast cancer transcriptomic database (see FIG. 43; Curtis et al.,2012). Additionally, it was observed that exposure of the MDA-MB-231basal-like/claudin-low cell line (low constitutive FOXC1 expressor) toWnt3a (a canonical Wnt signaling ligand), resulted in increasedexpression of FOXC1 (see FIG. 44D). Reciprocally, overexpression ofFOXC1 in MCF10A human mammary epithelial cells led to a pronouncedincrease in Wnt signaling activity, strongly suggestive of a direct orindirect positive feedback loop between Wnt signaling and FOXC1. Moreimportantly, BT549 and HS578T basal-like/claudin-low cells (highconstitutive FOXC1 expressors) proved to be more sensitive to treatmentwith the Wnt inhibitor iCRT3 as evidenced by decreased cell viabilitywhen compared to MCF7 (luminal) or SKBR3 (HER2) breast cancer cell lines(see FIGS. 45A and 45B). Furthermore the decrease in cell viabilityappeared to be proportionate to the level of FOXC1 expression (see FIGS.45A and 45B).

Upon pharmacological inhibition with iCRT3 and biological inhibitionwith siRNA knockdown of LRP6, a canonical Wnt signaling cell surfacereceptor, a decrease in FOXC1 expression level was observed in a doseand time dependent manner (see FIGS. 44A and 44B). This effect wasparticularly pronounced in mammosphere cultures enriched for BT549cancer stem-like cells. Inhibition of Wnt signaling reduced mammosphereformation efficiency of BT549 cells, suggesting that Wnt inhibitiontargets cancer stem cells (CSCs) in the basal-like/claudin-low breastcancer subtype (see FIGS. 47A and 47B). More importantly, however, afteran initial 4 day incubation period, some cells were observed to persistand later display renewed enhancement of mammosphere formation ability.Profiling of such cells interestingly revealed depletion of FOXC1positive cells, but persistence of cells displaying pronounced upregulation of stereotypical embryonic stem cell transcription factorsOCT4, SOX2 and NANOG, strongly suggestive of a potential primitive stemcell/quiescent cell state escape mechanism (see FIG. 48). qRT-PCR basedpathway activation analysis revealed marked activation of NF-κBsignaling in the residual cells that withstood Wnt inhibition.

Simultaneous pharmacologic inhibition with Wnt inhibitor iCRT3 and theproteasome inhibitor bortezomib, which is known to inhibit NF-κBsignaling, effectively targeted BT549 cancer stem cells in mammosphereculture and prevented the persistence/emergence of any residual cells(see FIGS. 49A and 49 B). The combination treatment of iCRT3 withbortezomib was so effective that no mammosphere cells were viable at theend of the combined treatment (i.e., iCRT3 and bortezomib) and thereforethere were no mammosphere cells to be profiled using qRT-PCR (see FIGS.49A and 49 B). Taken together, these findings suggest that combinationtherapy approaches are likely required to effectively target breastcancer stem cells.

In certain embodiments, the tumor cells of a patient with cancer may bescreened for FOXC1 expression to select patients with basal-likecancers. In certain embodiments, the patients may be treated with acombination therapy of a Wnt inhibitor and a proteasome inhibitor.

REFERENCES

The references listed below, and all references cited in thespecification are hereby incorporated by reference in their entirety.

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1. A method of theranostic classification of a breast cancer tumor, themethod comprising: obtaining a breast cancer tumor sample from asubject; detecting an expression level of FOXC1; comparing theexpression level of FOXC1 to a predetermined cutoff level; andclassifying the breast cancer tumor sample as belonging to a theranosticbasal-like breast cancer tumor subtype or a theranostic hybridbasal-like breast cancer tumor subtype when the expression level ofFOXC1 is higher than the predetermined cutoff level.
 2. The method ofclaim 1, wherein the breast cancer tumor sample is a formalin-fixedparaffin embedded (FFPE) sample.
 3. The method of claim 2, wherein theexpression level of FOXC1 is determined by quantitative reversetranscriptase polymerase chain reaction (qRT-PCR) or a Quantigene® FFPEassay.
 4. The method of claim 1, wherein the predetermined cutoff levelis determined by a 90th percentile level of FOXC1 expression levels fora dataset of breast cancer tumors, the dataset comprising all breastcancer subtypes.
 5. The method of claim 1, further comprising:determining an expression status for estrogen receptor (ER),progesterone receptor (PR) and human epidermal growth factor receptor 2(HER2); and classifying the breast cancer tumor sample as belonging to atheranostic hybrid basal-like/HER2+ breast cancer tumor subtype when theexpression status of ER is negative (ER−), the expression status of PRis negative (PR−), the expression status of HER2 is positive (HER2+) andthe expression level of FOXC1 is higher than the predetermined cutofflevel.
 6. The method of claim 5, wherein the predetermined cutoff levelis determined by a 50th percentile level of FOXC1 expression levels fora dataset of breast cancer tumors, the dataset comprising tumors havinga HER2+ status.
 7. The method of claim 1, further comprising:determining an expression status of ER, PR, and HER2 of the breastcancer tumor sample; and classifying the breast cancer tumor sample asbelonging to a theranostic hybrid basal-like/triple-negative breastcancer tumor subtype when the expression status of ER is negative (ER−),the expression status of PR is negative (PR−), the expression status ofHER2 is negative (HER2−) and the expression level of FOXC1 is higherthan the predetermined cutoff level.
 8. The method of claim 7, whereinthe predetermined cutoff level is determined by a 50th percentile levelof FOXC1 expression levels for a dataset of breast cancer tumors, thedataset comprising tumors having an ER−/PR−/HER2− status.
 9. The methodof claim 1, further comprising: determining an expression status of ER,PR, and HER2 of the breast cancer tumor sample; and classifying thebreast cancer tumor sample as belonging to a theranostic hybridbasal-like/luminal breast cancer tumor subtype when the expressionstatus of ER is positive (ER+), the expression status of PR is negativeor positive (PR−/PR+), the expression status of HER2 is negative orpositive (HER2−/HER2+) and the expression level of FOXC1 is higher thanthe predetermined cutoff level.
 10. The method of claim 9, wherein thepredetermined cutoff level is determined by a 50th percentile level ofFOXC1 expression levels for a dataset of breast cancer tumors, thedataset comprising tumors having an ER+ status.
 11. A method forpredicting a prognosis of a basal-like breast cancer, the methodcomprising: obtaining a breast cancer tumor sample from a subject;detecting an expression level of FOXC1; comparing the expression levelof FOXC1 to a predetermined cutoff level; predicting a poor prognosis ofthe basal-like breast cancer when the expression level of FOXC1 ishigher than the predetermined cutoff level.
 12. The method of claim 11,wherein the breast cancer tumor sample is a formalin-fixed paraffinembedded (FFPE) sample.
 13. The method of claim 12, wherein theexpression level of FOXC1 is determined by quantitative reversetranscriptase polymerase chain reaction (qRT-PCR) or a Quantigene® FFPEassay.
 14. The method of claim 11, wherein the predetermined cutofflevel is determined by a 90th percentile level of FOXC1 expressionlevels for a dataset of breast cancer tumors, the dataset comprising allbreast cancer subtypes.
 15. The method of claim 11, wherein thebasal-like breast cancer is a hybrid basal-like/HER2+ breast cancer andthe predetermined cutoff level is determined by a 50th percentile levelof FOXC1 expression levels for a dataset of breast cancer tumors, thedataset comprising tumors having a HER2+ status.
 16. The method of claim11, wherein the basal-like breast cancer is a hybrid basal-like/luminalbreast cancer and the predetermined cutoff level is determined by a 50thpercentile level of FOXC1 expression levels for a dataset of breastcancer tumors, the dataset comprising tumors having an ER+ status. 17.The method of claim 11, wherein the basal-like breast cancer is a hybridbasal-like/triple-negative breast cancer and the predetermined cutofflevel is determined by a 50th percentile level of FOXC1 expressionlevels for a dataset of breast cancer tumors, the dataset comprisingtumors having an ER−/PR−/HER2− status.
 18. The method of claim 11,wherein the prognosis is overall survival or recurrence free survival.19. The method of claim 11, wherein the prognosis is a propensity ofdeveloping a distant metastasis or a time to a distant metastasis. 20.The method of claim 19, wherein the distant metastasis is brainmetastasis.
 21. The method of claim 11, wherein the prognosis is apropensity for resistance to a targeted cancer therapy.
 22. The methodof claim 21, wherein the targeted therapy is a substance that inhibitsHER2 expression and/or activity or a substance that inhibits ERexpression and/or activity.
 23. The method of claim 22, wherein thesubstance that inhibits HER2 expression and/or activity is a trastuzumab(Herceptin®).
 24. The method of claim 22, wherein the substance thatinhibits ER expression and/or activity is tamoxifen or an aromataseinhibitor.